{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Our special classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Priors classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "class laplace_prior(object):\n",
    "    def __init__(self, mu, b):\n",
    "        self.mu = mu\n",
    "        self.b = b\n",
    "\n",
    "    def loglike(self, x, do_sum=True):\n",
    "        if do_sum:\n",
    "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b).sum()\n",
    "        else:\n",
    "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b)\n",
    "\n",
    "class isotropic_gauss_prior(object):\n",
    "    def __init__(self, mu, sigma):\n",
    "        self.mu = mu\n",
    "        self.sigma = sigma\n",
    "        \n",
    "        self.cte_term = -(0.5)*np.log(2*np.pi)\n",
    "        self.det_sig_term = -np.log(self.sigma)\n",
    "\n",
    "    def loglike(self, x, do_sum=True):\n",
    "        \n",
    "        dist_term = -(0.5)*((x - self.mu)/self.sigma)**2\n",
    "        if do_sum:\n",
    "            return (self.cte_term + self.det_sig_term + dist_term).sum()\n",
    "        else:\n",
    "            return (self.cte_term + self.det_sig_term + dist_term)\n",
    "    \n",
    "\n",
    "# TODO: adapt so can be done without sum\n",
    "class spike_slab_2GMM(object):\n",
    "    def __init__(self, mu1, mu2, sigma1, sigma2, pi):\n",
    "        \n",
    "        self.N1 = isotropic_gauss_prior(mu1, sigma1)\n",
    "        self.N2 = isotropic_gauss_prior(mu2, sigma2)\n",
    "        \n",
    "        self.pi1 = pi \n",
    "        self.pi2 = (1-pi)\n",
    "\n",
    "    def loglike(self, x):\n",
    "        \n",
    "        N1_ll = self.N1.loglike(x)\n",
    "        N2_ll = self.N2.loglike(x)\n",
    "        \n",
    "        # Numerical stability trick -> unnormalising logprobs will underflow otherwise\n",
    "        max_loglike = torch.max(N1_ll, N2_ll)\n",
    "        normalised_like = self.pi1 + torch.exp(N1_ll - max_loglike) + self.pi2 + torch.exp(N2_ll - max_loglike)\n",
    "        loglike = torch.log(normalised_like) + max_loglike\n",
    "    \n",
    "        return loglike\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isnan(tensor):\n",
    "  # Gross: https://github.com/pytorch/pytorch/issues/4767\n",
    "    return (tensor != tensor)\n",
    "\n",
    "def hasnan(tensor):\n",
    "    return isnan(tensor).any()\n",
    "\n",
    "def isotropic_gauss_loglike(x, mu, sigma, do_sum=True):\n",
    "    cte_term = -(0.5)*np.log(2*np.pi)\n",
    "    det_sig_term = -torch.log(sigma)\n",
    "    inner = (x - mu)/sigma\n",
    "    dist_term = -(0.5)*(inner**2)\n",
    "    \n",
    "    if do_sum:\n",
    "        out = (cte_term + det_sig_term + dist_term).sum() # sum over all weights\n",
    "    else:\n",
    "        out = (cte_term + det_sig_term + dist_term)\n",
    "#     print(hasnan(x.data), hasnan(mu.data), hasnan(sigma.data), out)\n",
    "    if isnan(out) or hasnan(out):\n",
    "        print('NAaaanNNN')\n",
    "        print('mu max', mu.max())\n",
    "        print('mu min', mu.min())\n",
    "        print('sigma max', sigma.max())\n",
    "        print('sigma min', sigma.min())\n",
    "        print('x max', x.max())\n",
    "        print('x min', x.min())\n",
    "        print((x - mu).max())\n",
    "        print((x - mu).min())\n",
    "        print(hasnan(inner))\n",
    "        print(dist_term)\n",
    "        print(hasnan(dist_term))\n",
    "    return out \n",
    "\n",
    "\n",
    "\n",
    "class BayesLinear_Normalq(nn.Module):\n",
    "    def __init__(self, n_in, n_out, prior_class):\n",
    "        super(BayesLinear_Normalq, self).__init__()\n",
    "        self.n_in = n_in\n",
    "        self.n_out = n_out\n",
    "        self.prior = prior_class\n",
    "        \n",
    "        # Learnable parameters\n",
    "        self.W_mu = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-0.2, 0.2))\n",
    "        self.W_p = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-3, -2))\n",
    "        \n",
    "        self.b_mu = nn.Parameter(torch.Tensor(self.n_out).uniform_(-0.2, 0.2))\n",
    "        self.b_p = nn.Parameter(torch.Tensor(self.n_out).uniform_(-3, -2))\n",
    "        \n",
    "        self.curr_W = None\n",
    "        self.curr_b = None\n",
    "       \n",
    "        self.lpw = 0\n",
    "        self.lqw = 0\n",
    "        \n",
    "                                   \n",
    "    def forward(self, X, sample=False):\n",
    "#         print(self.training)\n",
    "\n",
    "        if not self.training and not sample: # This is just a placeholder function\n",
    "            output = torch.mm(X, self.W_mu) + self.b_mu.expand(X.size()[0], self.n_out)\n",
    "            return output, 0, 0\n",
    "                                       \n",
    "        else:\n",
    "                              \n",
    "            # Tensor.new()  Constructs a new tensor of the same data type as self tensor. \n",
    "            # the same random sample is used for every element in the minibatch\n",
    "            eps_W = Variable(self.W_mu.data.new(self.W_mu.size()).normal_())\n",
    "            eps_b = Variable(self.b_mu.data.new(self.b_mu.size()).normal_())\n",
    "                                       \n",
    "            # sample parameters         \n",
    "            \n",
    "            std_w = 1e-6 + F.softplus(self.W_p, beta=1, threshold=20)\n",
    "            std_b = 1e-6 + F.softplus(self.b_p, beta=1, threshold=20)                      \n",
    "                                   \n",
    "            W = self.W_mu + 1 * std_w * eps_W\n",
    "            b = self.b_mu + 1 * std_b * eps_b          \n",
    "    \n",
    "            \n",
    "            output = torch.mm(X, W) + b.unsqueeze(0).expand(X.shape[0], -1) # (batch_size, n_output)\n",
    "            \n",
    "            lqw = isotropic_gauss_loglike(W, self.W_mu, std_w) + isotropic_gauss_loglike(b, self.b_mu, std_b)\n",
    "            lpw = self.prior.loglike(W) + self.prior.loglike(b)\n",
    "            return output, lqw, lpw\n",
    "    \n",
    "    def draw_sample(self):\n",
    "        eps_W = Variable(self.W_mu.data.new(self.W_mu.size()).normal_())\n",
    "        eps_b = Variable(self.b_mu.data.new(self.b_mu.size()).normal_())\n",
    "\n",
    "        # sample parameters         \n",
    "        std_w = 1e-6 + F.softplus(self.W_p, beta=1, threshold=20)\n",
    "        std_b = 1e-6 + F.softplus(self.b_p, beta=1, threshold=20)                      \n",
    "\n",
    "        self.curr_W = (self.W_mu + 1 * std_w * eps_W).detach()\n",
    "        self.curr_b = (self.b_mu + 1 * std_b * eps_b).detach()\n",
    "        \n",
    "        lqw = isotropic_gauss_loglike(self.curr_W, self.W_mu, std_w) + isotropic_gauss_loglike(self.curr_b, self.b_mu, std_b)\n",
    "        lpw = self.prior.loglike(self.curr_W) + self.prior.loglike(self.curr_b)\n",
    "        \n",
    "        return lqw, lpw\n",
    "        \n",
    "    \n",
    "    def forward_with_sample(self, X):\n",
    "        output = torch.mm(X, self.curr_W) + self.curr_b.unsqueeze(0).expand(X.shape[0], -1) # (batch_size, n_output)\n",
    "        return output\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick weight sampling function for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_weights(W_mu, b_mu, W_p, b_p):\n",
    "    \n",
    "    eps_W = W_mu.data.new(W_mu.size()).normal_()\n",
    "    # sample parameters         \n",
    "    std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)     \n",
    "    W = W_mu + 1 * std_w * eps_W\n",
    "    \n",
    "    if b_mu is not None:\n",
    "        std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)  \n",
    "        eps_b = b_mu.data.new(b_mu.size()).normal_()\n",
    "        b = b_mu + 1 * std_b * eps_b\n",
    "    else:\n",
    "        b = None\n",
    "    \n",
    "    return W, b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "class bayes_linear_2L(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(bayes_linear_2L, self).__init__()\n",
    "        \n",
    "        n_hid = 1200\n",
    "#         prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)\n",
    "#         prior_instance = spike_slab_2GMM(mu1=0, mu2=0, sigma1=0.135, sigma2=0.001, pi=0.5)\n",
    "        self.prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        \n",
    "        self.bfc1 = BayesLinear_Normalq(input_dim, n_hid, self.prior_instance)\n",
    "        self.bfc2 = BayesLinear_Normalq(n_hid, n_hid, self.prior_instance)\n",
    "        self.bfc3 = BayesLinear_Normalq(n_hid, output_dim, self.prior_instance)\n",
    "        \n",
    "        # choose your non linearity\n",
    "        #self.act = nn.Tanh()\n",
    "        #self.act = nn.Sigmoid()\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "        #self.act = nn.ELU(inplace=True)\n",
    "        #self.act = nn.SELU(inplace=True)\n",
    "\n",
    "    def forward(self, x, sample=False):\n",
    "        \n",
    "        tlqw = 0\n",
    "        tlpw = 0\n",
    "        \n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x, lqw, lpw = self.bfc1(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x, lqw, lpw = self.bfc2(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y, lqw, lpw = self.bfc3(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        \n",
    "        return y, tlqw, tlpw\n",
    "    \n",
    "    def draw_sample(self):\n",
    "        tlqw = 0\n",
    "        tlpw = 0\n",
    "        lqw, lpw = self.bfc1.draw_sample()\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        lqw, lpw = self.bfc2.draw_sample()\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        lqw, lpw = self.bfc3.draw_sample()\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "\n",
    "        param_dict = {}\n",
    "        param_dict['bfc1.W'] = self.bfc1.curr_W\n",
    "        param_dict['bfc1.b'] = self.bfc1.curr_b\n",
    "        param_dict['bfc2.W'] = self.bfc2.curr_W\n",
    "        param_dict['bfc2.b'] = self.bfc2.curr_b\n",
    "        param_dict['bfc3.W'] = self.bfc3.curr_W\n",
    "        param_dict['bfc3.b'] = self.bfc3.curr_b\n",
    "        \n",
    "        return param_dict, tlqw, tlpw\n",
    "    \n",
    "    def single_sample_predict(self, x, param_dict=None):\n",
    "        \n",
    "        if param_dict is not None:\n",
    "            self.bfc1.curr_W = param_dict['bfc1.W']\n",
    "            self.bfc1.curr_b = param_dict['bfc1.b']\n",
    "            self.bfc2.curr_W = param_dict['bfc2.W']\n",
    "            self.bfc2.curr_b = param_dict['bfc2.b']\n",
    "            self.bfc3.curr_W = param_dict['bfc3.W']\n",
    "            self.bfc3.curr_b = param_dict['bfc3.b']\n",
    "            \n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x = self.bfc1.forward_with_sample(x)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x = self.bfc2.forward_with_sample(x)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y = self.bfc3.forward_with_sample(x)\n",
    "        \n",
    "        return y\n",
    "        \n",
    "    \n",
    "    def sample_predict(self, x, sample_dict_vector):\n",
    "        Nsamples = len(sample_dict_vector)\n",
    "        # Just copies type from x, initializes new vector\n",
    "        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)\n",
    "        \n",
    "        for i, sample_dict in enumerate(sample_dict_vector):\n",
    "#             print(sample_dict)\n",
    "            y = self.single_sample_predict(x, param_dict=sample_dict)\n",
    "            predictions[i] = y \n",
    "            \n",
    "        return predictions\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IWAE weight computations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iwae_weights(unnormalised_weights):\n",
    "    samples = unnormalised_weights.shape[0]\n",
    "    # unnorm_weights: (samples)\n",
    "    \n",
    "    # compute Liw in numerically stable manner\n",
    "    max_log_weight = torch.max(unnormalised_weights)\n",
    "    unnormalised_weights_mm = unnormalised_weights - max_log_weight\n",
    "    # weight_normaliser: (scalar)\n",
    "    weight_normaliser = torch.sum(torch.exp(unnormalised_weights_mm))/samples\n",
    "    # weights: (samples)\n",
    "#     print(weight_normaliser)\n",
    "#     print(torch.exp(unnormalised_weights_mm))\n",
    "    weights = torch.exp(unnormalised_weights_mm)/weight_normaliser # numerator and denominator are divided by max\n",
    "\n",
    "    Liw = torch.log(weight_normaliser) + max_log_weight\n",
    "    \n",
    "    return Liw, weights\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import copy\n",
    "from torch.distributions.categorical import Categorical\n",
    "\n",
    "class Bayes_Net_IW(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, batch_size=128, Nbatches=0):\n",
    "        super(Bayes_Net_IW, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.channels_in = channels_in\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "        self.Nbatches = Nbatches\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = bayes_linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y, samples=10):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        if samples == 1: # Regular ELBO\n",
    "            out, tlqw, tlpw = self.model(x)\n",
    "            mlpdw = F.cross_entropy(out, y, reduction='sum')\n",
    "            Edkl = (tlqw - tlpw)/self.Nbatches\n",
    "            loss = Edkl + mlpdw\n",
    "            pred = pred = out.data.max(dim=1, keepdim=False)[1]\n",
    "            \n",
    "        elif samples > 1: # importance weighed numerically stable log sum exp trick\n",
    "            \n",
    "            preds = x.data.new(samples, x.shape[0], self.classes).zero_()\n",
    "    \n",
    "            for i in range(samples):\n",
    "                # out: (batch_size, out_channels)\n",
    "                out, tlqw, tlpw = self.model(x)\n",
    "                # preds: (samples, batch_size, self.classes)\n",
    "                preds[i] = F.softmax(out, dim=1)\n",
    "                mlpdw_i = F.cross_entropy(out, y, reduction='sum')\n",
    "                Edkl_i = (tlqw - tlpw)/self.Nbatches # keep this as we are still doing minibatch based opt\n",
    "                \n",
    "                if i == 0:# Accumulate results\n",
    "                    unnormalised_weights = (mlpdw_i + Edkl_i).unsqueeze(0)\n",
    "#                     print(unnormalised_weights.shape)\n",
    "                else:\n",
    "                    unnormalised_weights = torch.cat((unnormalised_weights, (mlpdw_i + Edkl_i).unsqueeze(0)), dim=0)\n",
    "#             print(unnormalised_weights.shape)\n",
    "                \n",
    "            Liw, weights = iwae_weights(unnormalised_weights)\n",
    "            loss = Liw    \n",
    "            \n",
    "            # We importance weigh the predictions in probability space\n",
    "            pred = (weights.unsqueeze(dim=1).unsqueeze(dim=1)*preds).sum(dim=0, keepdim=False).max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        \n",
    "        \n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err\n",
    "\n",
    "    def eval(self, x, y, train=False): # this just uses the mean of q\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    # This needs to be over the whole train set to evaluate the weights\n",
    "    # and the use a categorical to keep individual samples\n",
    "    def draw_weighed_samples(self, trainloader, Nsamples, k):\n",
    "        # Nsamples refers to how many samples we want from the posterior\n",
    "        # k refers to how many samples we weigh for each draw from the posterior\n",
    "        sample_dict_vector = []\n",
    "        for Nsamp in range(Nsamples):\n",
    "            print('drawing sample %d' % Nsamp)\n",
    "            sample_param_dict_vec = []\n",
    "            for i in range(k):\n",
    "                param_dict, tlqw, tlpw = self.model.draw_sample() # sample from weights\n",
    "                sample_param_dict_vec.append(copy.deepcopy(param_dict))\n",
    "                Edkl = (tlqw - tlpw)\n",
    "                mlpdw = 0\n",
    "            \n",
    "                for x, y in trainloader: # evaluate p(y|x,w) for all trainset\n",
    "                    x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "                    out = self.model.single_sample_predict(x) # predict with sampled weights\n",
    "                    mlpdw_i = F.cross_entropy(out, y, reduction='sum')\n",
    "                    mlpdw += mlpdw_i\n",
    "\n",
    "                if i == 0:# Accumulate results\n",
    "                    unnormalised_weights = (mlpdw + Edkl).unsqueeze(0)\n",
    "                else:\n",
    "                    unnormalised_weights = torch.cat((unnormalised_weights, (mlpdw + Edkl).unsqueeze(0)), dim=0)\n",
    "\n",
    "            Liw, weights = iwae_weights(unnormalised_weights)\n",
    "            sample_idx = Categorical(probs=weights).sample()\n",
    "            \n",
    "            sample_dict_vector.append(sample_param_dict_vec[sample_idx])\n",
    "            \n",
    "        return sample_dict_vector\n",
    "                \n",
    "                \n",
    "    \n",
    "    # we input a vector of dictionaries of parameters sampled from the reweighed approximate posterior\n",
    "    def sample_eval(self, x, y, sample_dict_vector, logits=True):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "        \n",
    "        out = self.model.sample_predict(x, sample_dict_vector)\n",
    "#         print(out.shape)\n",
    "#         print(out[0,:,:])\n",
    "        \n",
    "        if logits:\n",
    "            mean_out = out.mean(dim=0, keepdim=False)\n",
    "            loss = F.cross_entropy(mean_out, y, reduction='sum')\n",
    "            probs = F.softmax(mean_out, dim=1).data.cpu()\n",
    "            \n",
    "        else:\n",
    "            mean_out =  F.softmax(out, dim=2).mean(dim=0, keepdim=False)\n",
    "            probs = mean_out.data.cpu()\n",
    "            \n",
    "            log_mean_probs_out = torch.log(mean_out)\n",
    "            loss = F.nll_loss(log_mean_probs_out, y, reduction='sum')\n",
    "\n",
    "        pred = mean_out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    \n",
    "    def get_weight_samples(self, sample_dict_vector):\n",
    "        \n",
    "        for i, sample_dict in enumerate(sample_dict_vector):\n",
    "            previous_layer_name = ''\n",
    "            for key in sample_dict.keys():\n",
    "                layer_name = key.split('.')[0]\n",
    "                if layer_name != previous_layer_name:\n",
    "                    previous_layer_name = layer_name\n",
    "\n",
    "                    W = sample_dict[layer_name+'.W']\n",
    "#                     b = sample_dict[layer_name+'.b']\n",
    "\n",
    "                    for weight in W.cpu().view(-1):\n",
    "                        weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)\n",
    "    \n",
    "    def get_weight_SNR(self, thresh=None):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_SNR_vec = []\n",
    "        \n",
    "        if thresh is not None:\n",
    "            mask_dict = {}\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "\n",
    "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                W_p = state_dict[layer_name+'.W_p'].data\n",
    "                sig_W = 1e-6 + F.softplus(W_p, beta=1, threshold=20)\n",
    "\n",
    "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
    "                b_p = state_dict[layer_name+'.b_p'].data\n",
    "                sig_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
    "\n",
    "                W_snr = (torch.abs(W_mu)/sig_W)\n",
    "                b_snr = (torch.abs(b_mu)/sig_b)\n",
    "                \n",
    "                if thresh is not None:\n",
    "                    mask_dict[layer_name+'.W'] = W_snr > thresh\n",
    "                    mask_dict[layer_name+'.b'] = b_snr > thresh\n",
    "                    \n",
    "                else:\n",
    "                \n",
    "                    for weight_SNR in W_snr.cpu().view(-1):\n",
    "                        weight_SNR_vec.append(weight_SNR)\n",
    "\n",
    "                    for weight_SNR in b_snr.cpu().view(-1):\n",
    "                        weight_SNR_vec.append(weight_SNR)\n",
    "        \n",
    "        if thresh is not None:\n",
    "            return mask_dict\n",
    "        else:\n",
    "            return np.array(weight_SNR_vec)\n",
    "    \n",
    "    # This function would need to be reweighed\n",
    "#     def get_weight_KLD(self, Nsamples=20, thresh=None):\n",
    "#         state_dict = self.model.state_dict()\n",
    "#         weight_KLD_vec = []\n",
    "        \n",
    "#         if thresh is not None:\n",
    "#             mask_dict = {}\n",
    "        \n",
    "#         previous_layer_name = ''\n",
    "#         for key in state_dict.keys():\n",
    "#             layer_name = key.split('.')[0]\n",
    "#             if layer_name != previous_layer_name:\n",
    "#                 previous_layer_name = layer_name\n",
    "\n",
    "#                 W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "#                 W_p = state_dict[layer_name+'.W_p'].data\n",
    "#                 b_mu = state_dict[layer_name+'.b_mu'].data\n",
    "#                 b_p = state_dict[layer_name+'.b_p'].data\n",
    "                \n",
    "#                 std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)  \n",
    "#                 std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
    "\n",
    "#                 KL_W = W_mu.new(W_mu.size()).zero_()\n",
    "#                 KL_b = b_mu.new(b_mu.size()).zero_()\n",
    "#                 for i in range(Nsamples):\n",
    "#                     W, b = sample_weights(W_mu=W_mu, b_mu=b_mu, W_p=W_p, b_p=b_p)  \n",
    "#                     # Note that this will currently not work with slab and spike prior\n",
    "#                     KL_W += isotropic_gauss_loglike(W, W_mu, std_w, do_sum=False) - self.model.prior_instance.loglike(W, do_sum=False)\n",
    "#                     KL_b += isotropic_gauss_loglike(b, b_mu, std_b, do_sum=False) - self.model.prior_instance.loglike(b, do_sum=False)\n",
    "\n",
    "#                 KL_W /= Nsamples\n",
    "#                 KL_b /= Nsamples\n",
    "                \n",
    "#                 if thresh is not None:\n",
    "#                     mask_dict[layer_name+'.W'] = KL_W > thresh\n",
    "#                     mask_dict[layer_name+'.b'] = KL_b > thresh\n",
    "                    \n",
    "#                 else:\n",
    "\n",
    "#                     for weight_KLD in KL_W.cpu().view(-1):\n",
    "#                         weight_KLD_vec.append(weight_KLD)\n",
    "\n",
    "#                     for weight_KLD in KL_b.cpu().view(-1):\n",
    "#                         weight_KLD_vec.append(weight_KLD)\n",
    "        \n",
    "#         if thresh is not None:\n",
    "#             return mask_dict\n",
    "#         else:    \n",
    "#             return np.array(weight_KLD_vec)\n",
    "        \n",
    "        \n",
    "    def mask_model(self, Nsamples=0, thresh=0):\n",
    "        '''\n",
    "        Nsamples is used to select SNR (0) or KLD (>0) based masking\n",
    "        '''\n",
    "        original_state_dict = copy.deepcopy(self.model.state_dict())\n",
    "        state_dict = self.model.state_dict()\n",
    "        \n",
    "        if Nsamples == 0:\n",
    "            mask_dict = self.get_weight_SNR(thresh=thresh)\n",
    "        else:\n",
    "            mask_dict = self.get_weight_KLD(Nsamples=Nsamples, thresh=thresh)\n",
    "        \n",
    "        n_unmasked = 0\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "                \n",
    "                state_dict[layer_name+'.W_mu'][1-mask_dict[layer_name+'.W']] = 0\n",
    "                state_dict[layer_name+'.W_p'][1-mask_dict[layer_name+'.W']] = -1000\n",
    "                \n",
    "                state_dict[layer_name+'.b_mu'][1-mask_dict[layer_name+'.b']] = 0\n",
    "                state_dict[layer_name+'.b_p'][1-mask_dict[layer_name+'.b']] = -1000\n",
    "                \n",
    "                \n",
    "                n_unmasked += mask_dict[layer_name+'.W'].sum()\n",
    "                n_unmasked += mask_dict[layer_name+'.b'].sum()\n",
    "                \n",
    "        return original_state_dict, n_unmasked\n",
    "            \n",
    "            \n",
    "            \n",
    "        \n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aa = net.model.state_dict()\n",
    "# net.model.load_state_dict(aa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(net.model.state_dict()['bfc1.W_mu'].cpu().data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import copy\n",
    "# aaa = copy.deepcopy(net.model)\n",
    "# net.model = aaa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 4.79M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 0/300, Jtr = 50.533550, err = 0.210783, \u001b[31m   time: 12.008403 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.289258, err = 0.080200\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 1/300, Jtr = 33.625327, err = 0.115467, \u001b[31m   time: 12.060184 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.233600, err = 0.069300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 2/300, Jtr = 28.273023, err = 0.097300, \u001b[31m   time: 11.900645 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.192058, err = 0.057300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 3/300, Jtr = 23.914650, err = 0.088617, \u001b[31m   time: 12.023424 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.189657, err = 0.058000\n",
      "\u001b[0m\n",
      "it 4/300, Jtr = 20.349087, err = 0.083900, \u001b[31m   time: 11.869762 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.164596, err = 0.051300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 5/300, Jtr = 17.438147, err = 0.079917, \u001b[31m   time: 11.877593 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.161729, err = 0.051300\n",
      "\u001b[0m\n",
      "it 6/300, Jtr = 15.055864, err = 0.078917, \u001b[31m   time: 12.040237 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.159362, err = 0.050400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 7/300, Jtr = 13.106769, err = 0.076950, \u001b[31m   time: 10.543246 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.146465, err = 0.044900\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 8/300, Jtr = 11.508664, err = 0.073700, \u001b[31m   time: 10.252532 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.146628, err = 0.047100\n",
      "\u001b[0m\n",
      "it 9/300, Jtr = 10.202606, err = 0.074017, \u001b[31m   time: 10.918985 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169801, err = 0.053400\n",
      "\u001b[0m\n",
      "it 10/300, Jtr = 9.127656, err = 0.071717, \u001b[31m   time: 12.196973 seconds\n",
      "\u001b[0m\n",
      "now running IW with 5 samples\n",
      "\u001b[32m    Jdev = 0.151705, err = 0.049300\n",
      "\u001b[0m\n",
      "it 11/300, Jtr = 8.314972, err = 0.081950, \u001b[31m   time: 48.106929 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.160389, err = 0.046800\n",
      "\u001b[0m\n",
      "it 12/300, Jtr = 7.594790, err = 0.079183, \u001b[31m   time: 48.231488 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.129331, err = 0.038800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 13/300, Jtr = 7.005494, err = 0.078267, \u001b[31m   time: 48.662628 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.142597, err = 0.042800\n",
      "\u001b[0m\n",
      "it 14/300, Jtr = 6.516644, err = 0.075067, \u001b[31m   time: 48.050959 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.131911, err = 0.041000\n",
      "\u001b[0m\n",
      "it 15/300, Jtr = 6.120223, err = 0.075750, \u001b[31m   time: 47.913716 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.126412, err = 0.039800\n",
      "\u001b[0m\n",
      "it 16/300, Jtr = 5.787986, err = 0.072917, \u001b[31m   time: 47.941701 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.119401, err = 0.036700\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 17/300, Jtr = 5.520989, err = 0.074750, \u001b[31m   time: 48.008820 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.136198, err = 0.040800\n",
      "\u001b[0m\n",
      "it 18/300, Jtr = 5.296463, err = 0.072017, \u001b[31m   time: 47.911196 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.129802, err = 0.041000\n",
      "\u001b[0m\n",
      "it 19/300, Jtr = 5.116392, err = 0.071750, \u001b[31m   time: 47.587070 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.123940, err = 0.038700\n",
      "\u001b[0m\n",
      "it 20/300, Jtr = 4.963964, err = 0.070267, \u001b[31m   time: 47.514416 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.117277, err = 0.037100\n",
      "\u001b[0m\n",
      "it 21/300, Jtr = 4.836168, err = 0.070767, \u001b[31m   time: 47.593200 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.116007, err = 0.036200\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 22/300, Jtr = 4.730967, err = 0.068917, \u001b[31m   time: 47.871715 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.113483, err = 0.035400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 23/300, Jtr = 4.641219, err = 0.068550, \u001b[31m   time: 47.796606 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.117352, err = 0.034700\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 24/300, Jtr = 4.565891, err = 0.068067, \u001b[31m   time: 47.573069 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.114880, err = 0.035500\n",
      "\u001b[0m\n",
      "it 25/300, Jtr = 4.510308, err = 0.070050, \u001b[31m   time: 47.572131 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.128518, err = 0.039300\n",
      "\u001b[0m\n",
      "it 26/300, Jtr = 4.450959, err = 0.067017, \u001b[31m   time: 47.658051 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.110269, err = 0.034700\n",
      "\u001b[0m\n",
      "it 27/300, Jtr = 4.410976, err = 0.067350, \u001b[31m   time: 47.650008 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108263, err = 0.033400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 28/300, Jtr = 4.370280, err = 0.065817, \u001b[31m   time: 47.613997 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.110660, err = 0.035300\n",
      "\u001b[0m\n",
      "it 29/300, Jtr = 4.337194, err = 0.066017, \u001b[31m   time: 47.614675 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107006, err = 0.034500\n",
      "\u001b[0m\n",
      "it 30/300, Jtr = 4.306946, err = 0.066567, \u001b[31m   time: 47.581028 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107931, err = 0.032100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 31/300, Jtr = 4.283167, err = 0.066233, \u001b[31m   time: 47.590332 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103368, err = 0.031800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 32/300, Jtr = 4.259544, err = 0.065883, \u001b[31m   time: 47.534410 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103126, err = 0.030600\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 33/300, Jtr = 4.243092, err = 0.066233, \u001b[31m   time: 47.458380 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.118722, err = 0.038800\n",
      "\u001b[0m\n",
      "it 34/300, Jtr = 4.222267, err = 0.064700, \u001b[31m   time: 47.546799 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105317, err = 0.031000\n",
      "\u001b[0m\n",
      "it 35/300, Jtr = 4.205720, err = 0.065817, \u001b[31m   time: 47.459438 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106242, err = 0.033700\n",
      "\u001b[0m\n",
      "it 36/300, Jtr = 4.187143, err = 0.063867, \u001b[31m   time: 47.530786 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108588, err = 0.034000\n",
      "\u001b[0m\n",
      "it 37/300, Jtr = 4.174781, err = 0.063917, \u001b[31m   time: 47.322349 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.113668, err = 0.034900\n",
      "\u001b[0m\n",
      "it 38/300, Jtr = 4.161063, err = 0.063783, \u001b[31m   time: 47.495703 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.117565, err = 0.036200\n",
      "\u001b[0m\n",
      "it 39/300, Jtr = 4.148852, err = 0.063450, \u001b[31m   time: 47.484277 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.113594, err = 0.035000\n",
      "\u001b[0m\n",
      "it 40/300, Jtr = 4.138267, err = 0.062667, \u001b[31m   time: 47.508711 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.113844, err = 0.034100\n",
      "\u001b[0m\n",
      "it 41/300, Jtr = 4.127490, err = 0.063850, \u001b[31m   time: 47.326312 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106299, err = 0.031800\n",
      "\u001b[0m\n",
      "it 42/300, Jtr = 4.113329, err = 0.062050, \u001b[31m   time: 47.587034 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098761, err = 0.028700\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 43/300, Jtr = 4.103905, err = 0.063050, \u001b[31m   time: 47.612909 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105013, err = 0.032700\n",
      "\u001b[0m\n",
      "it 44/300, Jtr = 4.096394, err = 0.063950, \u001b[31m   time: 47.734623 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.117045, err = 0.036200\n",
      "\u001b[0m\n",
      "it 45/300, Jtr = 4.079041, err = 0.060000, \u001b[31m   time: 47.817797 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098485, err = 0.030400\n",
      "\u001b[0m\n",
      "it 46/300, Jtr = 4.071348, err = 0.062517, \u001b[31m   time: 48.114082 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101296, err = 0.029200\n",
      "\u001b[0m\n",
      "it 47/300, Jtr = 4.062190, err = 0.061783, \u001b[31m   time: 47.877564 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104281, err = 0.031100\n",
      "\u001b[0m\n",
      "it 48/300, Jtr = 4.054459, err = 0.063083, \u001b[31m   time: 48.083201 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.110375, err = 0.034600\n",
      "\u001b[0m\n",
      "it 49/300, Jtr = 4.041683, err = 0.062367, \u001b[31m   time: 48.156241 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.113396, err = 0.035500\n",
      "\u001b[0m\n",
      "it 50/300, Jtr = 4.035427, err = 0.061250, \u001b[31m   time: 47.751198 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107172, err = 0.030400\n",
      "\u001b[0m\n",
      "it 51/300, Jtr = 4.024046, err = 0.060767, \u001b[31m   time: 47.860865 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.111827, err = 0.035200\n",
      "\u001b[0m\n",
      "it 52/300, Jtr = 4.015275, err = 0.061583, \u001b[31m   time: 47.759104 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098316, err = 0.029700\n",
      "\u001b[0m\n",
      "it 53/300, Jtr = 4.004352, err = 0.060633, \u001b[31m   time: 47.834463 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.115551, err = 0.035600\n",
      "\u001b[0m\n",
      "it 54/300, Jtr = 4.001209, err = 0.061833, \u001b[31m   time: 47.796493 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099935, err = 0.031200\n",
      "\u001b[0m\n",
      "it 55/300, Jtr = 3.990665, err = 0.061133, \u001b[31m   time: 43.928926 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098827, err = 0.028500\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 56/300, Jtr = 3.980276, err = 0.061183, \u001b[31m   time: 47.484822 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093818, err = 0.028000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 57/300, Jtr = 3.974872, err = 0.060867, \u001b[31m   time: 47.397378 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098990, err = 0.030000\n",
      "\u001b[0m\n",
      "it 58/300, Jtr = 3.965480, err = 0.060333, \u001b[31m   time: 47.562764 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106745, err = 0.032600\n",
      "\u001b[0m\n",
      "it 59/300, Jtr = 3.954086, err = 0.060400, \u001b[31m   time: 47.585065 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103215, err = 0.030200\n",
      "\u001b[0m\n",
      "it 60/300, Jtr = 3.948681, err = 0.060650, \u001b[31m   time: 47.512594 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101120, err = 0.030800\n",
      "\u001b[0m\n",
      "it 61/300, Jtr = 3.939575, err = 0.060867, \u001b[31m   time: 47.479374 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m    Jdev = 0.102769, err = 0.030300\n",
      "\u001b[0m\n",
      "it 62/300, Jtr = 3.930248, err = 0.060250, \u001b[31m   time: 47.376634 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108436, err = 0.033100\n",
      "\u001b[0m\n",
      "it 63/300, Jtr = 3.922772, err = 0.059933, \u001b[31m   time: 47.513127 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099298, err = 0.029100\n",
      "\u001b[0m\n",
      "it 64/300, Jtr = 3.911564, err = 0.059367, \u001b[31m   time: 47.652293 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.112615, err = 0.036300\n",
      "\u001b[0m\n",
      "it 65/300, Jtr = 3.907884, err = 0.061250, \u001b[31m   time: 47.690200 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108113, err = 0.032700\n",
      "\u001b[0m\n",
      "it 66/300, Jtr = 3.899186, err = 0.060917, \u001b[31m   time: 44.625777 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101602, err = 0.030500\n",
      "\u001b[0m\n",
      "it 67/300, Jtr = 3.888631, err = 0.059850, \u001b[31m   time: 47.874835 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.121202, err = 0.038600\n",
      "\u001b[0m\n",
      "it 68/300, Jtr = 3.879681, err = 0.058567, \u001b[31m   time: 48.030636 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103673, err = 0.030200\n",
      "\u001b[0m\n",
      "it 69/300, Jtr = 3.871331, err = 0.058967, \u001b[31m   time: 47.814276 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096666, err = 0.028400\n",
      "\u001b[0m\n",
      "it 70/300, Jtr = 3.866211, err = 0.060983, \u001b[31m   time: 47.878511 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107608, err = 0.033600\n",
      "\u001b[0m\n",
      "it 71/300, Jtr = 3.859218, err = 0.059717, \u001b[31m   time: 47.877485 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107235, err = 0.034000\n",
      "\u001b[0m\n",
      "it 72/300, Jtr = 3.849725, err = 0.059383, \u001b[31m   time: 47.938114 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103305, err = 0.030200\n",
      "\u001b[0m\n",
      "it 73/300, Jtr = 3.841014, err = 0.059150, \u001b[31m   time: 47.717283 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095660, err = 0.028500\n",
      "\u001b[0m\n",
      "it 74/300, Jtr = 3.831659, err = 0.058983, \u001b[31m   time: 47.741071 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101731, err = 0.031700\n",
      "\u001b[0m\n",
      "it 75/300, Jtr = 3.826445, err = 0.058900, \u001b[31m   time: 47.873703 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099056, err = 0.030600\n",
      "\u001b[0m\n",
      "it 76/300, Jtr = 3.814961, err = 0.057900, \u001b[31m   time: 47.849027 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107892, err = 0.033600\n",
      "\u001b[0m\n",
      "it 77/300, Jtr = 3.814606, err = 0.061150, \u001b[31m   time: 48.118992 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106909, err = 0.033400\n",
      "\u001b[0m\n",
      "it 78/300, Jtr = 3.798878, err = 0.058800, \u001b[31m   time: 47.729059 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101288, err = 0.032100\n",
      "\u001b[0m\n",
      "it 79/300, Jtr = 3.798073, err = 0.060883, \u001b[31m   time: 47.808424 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101179, err = 0.029300\n",
      "\u001b[0m\n",
      "it 80/300, Jtr = 3.783738, err = 0.058933, \u001b[31m   time: 47.907845 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093548, err = 0.027400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 81/300, Jtr = 3.776220, err = 0.058817, \u001b[31m   time: 47.749917 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095527, err = 0.027800\n",
      "\u001b[0m\n",
      "it 82/300, Jtr = 3.770654, err = 0.058483, \u001b[31m   time: 47.880683 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100649, err = 0.030600\n",
      "\u001b[0m\n",
      "it 83/300, Jtr = 3.761959, err = 0.057600, \u001b[31m   time: 47.732987 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.112802, err = 0.035000\n",
      "\u001b[0m\n",
      "it 84/300, Jtr = 3.753140, err = 0.058933, \u001b[31m   time: 47.762595 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096034, err = 0.028200\n",
      "\u001b[0m\n",
      "it 85/300, Jtr = 3.745336, err = 0.058450, \u001b[31m   time: 47.894503 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097541, err = 0.027100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 86/300, Jtr = 3.738132, err = 0.059317, \u001b[31m   time: 47.882681 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101584, err = 0.030800\n",
      "\u001b[0m\n",
      "it 87/300, Jtr = 3.731621, err = 0.059483, \u001b[31m   time: 47.855334 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104768, err = 0.030800\n",
      "\u001b[0m\n",
      "it 88/300, Jtr = 3.726029, err = 0.058850, \u001b[31m   time: 47.602689 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103225, err = 0.032000\n",
      "\u001b[0m\n",
      "it 89/300, Jtr = 3.715025, err = 0.058483, \u001b[31m   time: 47.400259 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099564, err = 0.028300\n",
      "\u001b[0m\n",
      "it 90/300, Jtr = 3.709394, err = 0.057817, \u001b[31m   time: 47.366219 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106593, err = 0.031500\n",
      "\u001b[0m\n",
      "it 91/300, Jtr = 3.699018, err = 0.058400, \u001b[31m   time: 47.554167 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099402, err = 0.029700\n",
      "\u001b[0m\n",
      "it 92/300, Jtr = 3.690113, err = 0.056783, \u001b[31m   time: 47.467363 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101212, err = 0.031100\n",
      "\u001b[0m\n",
      "it 93/300, Jtr = 3.684234, err = 0.058533, \u001b[31m   time: 47.426950 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.110769, err = 0.034600\n",
      "\u001b[0m\n",
      "it 94/300, Jtr = 3.678243, err = 0.058833, \u001b[31m   time: 47.388208 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098268, err = 0.028600\n",
      "\u001b[0m\n",
      "it 95/300, Jtr = 3.671792, err = 0.059000, \u001b[31m   time: 47.485368 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.112841, err = 0.033200\n",
      "\u001b[0m\n",
      "it 96/300, Jtr = 3.661317, err = 0.057350, \u001b[31m   time: 47.963727 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106501, err = 0.030800\n",
      "\u001b[0m\n",
      "it 97/300, Jtr = 3.654678, err = 0.056667, \u001b[31m   time: 47.865159 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.111137, err = 0.034300\n",
      "\u001b[0m\n",
      "it 98/300, Jtr = 3.648409, err = 0.057833, \u001b[31m   time: 47.983706 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.116115, err = 0.032800\n",
      "\u001b[0m\n",
      "it 99/300, Jtr = 3.640581, err = 0.057067, \u001b[31m   time: 48.006367 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104586, err = 0.031900\n",
      "\u001b[0m\n",
      "it 100/300, Jtr = 3.631934, err = 0.057350, \u001b[31m   time: 48.060110 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104525, err = 0.031200\n",
      "\u001b[0m\n",
      "it 101/300, Jtr = 3.626039, err = 0.058217, \u001b[31m   time: 48.132619 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.112077, err = 0.033800\n",
      "\u001b[0m\n",
      "it 102/300, Jtr = 3.617204, err = 0.056717, \u001b[31m   time: 43.974965 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102315, err = 0.030700\n",
      "\u001b[0m\n",
      "it 103/300, Jtr = 3.610879, err = 0.057617, \u001b[31m   time: 47.490121 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107873, err = 0.033200\n",
      "\u001b[0m\n",
      "it 104/300, Jtr = 3.602986, err = 0.057583, \u001b[31m   time: 47.543402 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107080, err = 0.034100\n",
      "\u001b[0m\n",
      "it 105/300, Jtr = 3.594821, err = 0.056017, \u001b[31m   time: 47.513155 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106123, err = 0.032800\n",
      "\u001b[0m\n",
      "it 106/300, Jtr = 3.588972, err = 0.057483, \u001b[31m   time: 47.440974 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091777, err = 0.026800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 107/300, Jtr = 3.581092, err = 0.057933, \u001b[31m   time: 47.817970 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095411, err = 0.028700\n",
      "\u001b[0m\n",
      "it 108/300, Jtr = 3.575196, err = 0.057617, \u001b[31m   time: 48.195885 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.109662, err = 0.032700\n",
      "\u001b[0m\n",
      "it 109/300, Jtr = 3.568650, err = 0.057767, \u001b[31m   time: 47.785677 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100258, err = 0.029900\n",
      "\u001b[0m\n",
      "it 110/300, Jtr = 3.559843, err = 0.058533, \u001b[31m   time: 47.968080 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099487, err = 0.030200\n",
      "\u001b[0m\n",
      "it 111/300, Jtr = 3.554238, err = 0.058133, \u001b[31m   time: 47.945432 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099461, err = 0.031500\n",
      "\u001b[0m\n",
      "it 112/300, Jtr = 3.544826, err = 0.056667, \u001b[31m   time: 47.687907 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103177, err = 0.030400\n",
      "\u001b[0m\n",
      "it 113/300, Jtr = 3.541986, err = 0.057400, \u001b[31m   time: 47.864884 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095679, err = 0.029500\n",
      "\u001b[0m\n",
      "it 114/300, Jtr = 3.534307, err = 0.057700, \u001b[31m   time: 47.864170 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092602, err = 0.027400\n",
      "\u001b[0m\n",
      "it 115/300, Jtr = 3.522352, err = 0.056683, \u001b[31m   time: 47.830287 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097402, err = 0.027600\n",
      "\u001b[0m\n",
      "it 116/300, Jtr = 3.515205, err = 0.056233, \u001b[31m   time: 47.878039 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102364, err = 0.031400\n",
      "\u001b[0m\n",
      "it 117/300, Jtr = 3.509207, err = 0.055967, \u001b[31m   time: 48.010521 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095544, err = 0.028400\n",
      "\u001b[0m\n",
      "it 118/300, Jtr = 3.505932, err = 0.057517, \u001b[31m   time: 48.008705 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101871, err = 0.031000\n",
      "\u001b[0m\n",
      "it 119/300, Jtr = 3.496030, err = 0.057017, \u001b[31m   time: 47.771991 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098980, err = 0.030600\n",
      "\u001b[0m\n",
      "it 120/300, Jtr = 3.487159, err = 0.056400, \u001b[31m   time: 47.876960 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102996, err = 0.031200\n",
      "\u001b[0m\n",
      "it 121/300, Jtr = 3.481793, err = 0.057733, \u001b[31m   time: 47.694225 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.116479, err = 0.035400\n",
      "\u001b[0m\n",
      "it 122/300, Jtr = 3.477695, err = 0.057433, \u001b[31m   time: 47.437139 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102288, err = 0.031800\n",
      "\u001b[0m\n",
      "it 123/300, Jtr = 3.471040, err = 0.057383, \u001b[31m   time: 47.575515 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093376, err = 0.028000\n",
      "\u001b[0m\n",
      "it 124/300, Jtr = 3.459733, err = 0.056600, \u001b[31m   time: 47.531496 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095881, err = 0.028600\n",
      "\u001b[0m\n",
      "it 125/300, Jtr = 3.451468, err = 0.055733, \u001b[31m   time: 47.554928 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099922, err = 0.030200\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 126/300, Jtr = 3.447041, err = 0.057000, \u001b[31m   time: 47.967497 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105028, err = 0.030000\n",
      "\u001b[0m\n",
      "it 127/300, Jtr = 3.438153, err = 0.056350, \u001b[31m   time: 48.051474 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099926, err = 0.031700\n",
      "\u001b[0m\n",
      "it 128/300, Jtr = 3.432706, err = 0.056833, \u001b[31m   time: 48.029021 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.126160, err = 0.037300\n",
      "\u001b[0m\n",
      "it 129/300, Jtr = 3.428525, err = 0.058133, \u001b[31m   time: 48.487513 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102264, err = 0.031000\n",
      "\u001b[0m\n",
      "it 130/300, Jtr = 3.418637, err = 0.056217, \u001b[31m   time: 48.121799 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098540, err = 0.030000\n",
      "\u001b[0m\n",
      "it 131/300, Jtr = 3.414672, err = 0.056467, \u001b[31m   time: 47.939317 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100027, err = 0.030500\n",
      "\u001b[0m\n",
      "it 132/300, Jtr = 3.407079, err = 0.057167, \u001b[31m   time: 47.800140 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101309, err = 0.030700\n",
      "\u001b[0m\n",
      "it 133/300, Jtr = 3.403102, err = 0.057400, \u001b[31m   time: 48.014963 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100546, err = 0.028700\n",
      "\u001b[0m\n",
      "it 134/300, Jtr = 3.394046, err = 0.056300, \u001b[31m   time: 48.045737 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095871, err = 0.028200\n",
      "\u001b[0m\n",
      "it 135/300, Jtr = 3.387889, err = 0.055750, \u001b[31m   time: 47.966970 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102042, err = 0.030600\n",
      "\u001b[0m\n",
      "it 136/300, Jtr = 3.377822, err = 0.056600, \u001b[31m   time: 47.925366 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105680, err = 0.032700\n",
      "\u001b[0m\n",
      "it 137/300, Jtr = 3.372218, err = 0.056450, \u001b[31m   time: 48.082387 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102953, err = 0.029900\n",
      "\u001b[0m\n",
      "it 138/300, Jtr = 3.363290, err = 0.056117, \u001b[31m   time: 47.681634 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095695, err = 0.029100\n",
      "\u001b[0m\n",
      "it 139/300, Jtr = 3.358844, err = 0.056183, \u001b[31m   time: 47.876365 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105590, err = 0.031100\n",
      "\u001b[0m\n",
      "it 140/300, Jtr = 3.351972, err = 0.056100, \u001b[31m   time: 48.084394 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091993, err = 0.027400\n",
      "\u001b[0m\n",
      "it 141/300, Jtr = 3.344754, err = 0.056133, \u001b[31m   time: 47.807195 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090456, err = 0.027000\n",
      "\u001b[0m\n",
      "it 142/300, Jtr = 3.339465, err = 0.057067, \u001b[31m   time: 47.932445 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102450, err = 0.031200\n",
      "\u001b[0m\n",
      "it 143/300, Jtr = 3.333413, err = 0.056533, \u001b[31m   time: 47.812760 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.111058, err = 0.035100\n",
      "\u001b[0m\n",
      "it 144/300, Jtr = 3.322555, err = 0.054583, \u001b[31m   time: 47.886333 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100413, err = 0.030200\n",
      "\u001b[0m\n",
      "it 145/300, Jtr = 3.322470, err = 0.057617, \u001b[31m   time: 47.929519 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096109, err = 0.027400\n",
      "\u001b[0m\n",
      "it 146/300, Jtr = 3.309205, err = 0.055000, \u001b[31m   time: 44.220778 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103433, err = 0.031100\n",
      "\u001b[0m\n",
      "it 147/300, Jtr = 3.305757, err = 0.056783, \u001b[31m   time: 47.543069 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096590, err = 0.027600\n",
      "\u001b[0m\n",
      "it 148/300, Jtr = 3.293764, err = 0.054850, \u001b[31m   time: 47.549380 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096774, err = 0.027600\n",
      "\u001b[0m\n",
      "it 149/300, Jtr = 3.290722, err = 0.056350, \u001b[31m   time: 47.419998 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094283, err = 0.027200\n",
      "\u001b[0m\n",
      "it 150/300, Jtr = 3.286260, err = 0.055867, \u001b[31m   time: 47.550848 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099196, err = 0.031000\n",
      "\u001b[0m\n",
      "it 151/300, Jtr = 3.277433, err = 0.055800, \u001b[31m   time: 47.619505 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091050, err = 0.026800\n",
      "\u001b[0m\n",
      "it 152/300, Jtr = 3.270415, err = 0.055917, \u001b[31m   time: 47.650717 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091347, err = 0.028100\n",
      "\u001b[0m\n",
      "it 153/300, Jtr = 3.266964, err = 0.055867, \u001b[31m   time: 47.525129 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093257, err = 0.027600\n",
      "\u001b[0m\n",
      "it 154/300, Jtr = 3.255516, err = 0.055467, \u001b[31m   time: 47.564908 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101779, err = 0.030800\n",
      "\u001b[0m\n",
      "it 155/300, Jtr = 3.252321, err = 0.056567, \u001b[31m   time: 47.577214 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100011, err = 0.030500\n",
      "\u001b[0m\n",
      "it 156/300, Jtr = 3.246732, err = 0.056217, \u001b[31m   time: 47.573090 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101644, err = 0.032700\n",
      "\u001b[0m\n",
      "it 157/300, Jtr = 3.238835, err = 0.054433, \u001b[31m   time: 47.583389 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096027, err = 0.028200\n",
      "\u001b[0m\n",
      "it 158/300, Jtr = 3.233784, err = 0.055683, \u001b[31m   time: 47.486140 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095192, err = 0.029200\n",
      "\u001b[0m\n",
      "it 159/300, Jtr = 3.227348, err = 0.056433, \u001b[31m   time: 47.509727 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106169, err = 0.034000\n",
      "\u001b[0m\n",
      "it 160/300, Jtr = 3.220265, err = 0.056317, \u001b[31m   time: 47.563377 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096636, err = 0.028900\n",
      "\u001b[0m\n",
      "it 161/300, Jtr = 3.214555, err = 0.055617, \u001b[31m   time: 47.509504 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105332, err = 0.031600\n",
      "\u001b[0m\n",
      "it 162/300, Jtr = 3.204310, err = 0.054833, \u001b[31m   time: 47.391616 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090370, err = 0.026100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 163/300, Jtr = 3.200883, err = 0.055000, \u001b[31m   time: 47.508045 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094859, err = 0.027700\n",
      "\u001b[0m\n",
      "it 164/300, Jtr = 3.195106, err = 0.056417, \u001b[31m   time: 48.103007 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100939, err = 0.031900\n",
      "\u001b[0m\n",
      "it 165/300, Jtr = 3.185176, err = 0.054333, \u001b[31m   time: 48.123542 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108395, err = 0.034200\n",
      "\u001b[0m\n",
      "it 166/300, Jtr = 3.183419, err = 0.055700, \u001b[31m   time: 48.173150 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105083, err = 0.032000\n",
      "\u001b[0m\n",
      "it 167/300, Jtr = 3.177036, err = 0.054333, \u001b[31m   time: 48.178263 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090054, err = 0.026100\n",
      "\u001b[0m\n",
      "it 168/300, Jtr = 3.167636, err = 0.055550, \u001b[31m   time: 48.012562 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101244, err = 0.029300\n",
      "\u001b[0m\n",
      "it 169/300, Jtr = 3.163632, err = 0.055233, \u001b[31m   time: 47.925875 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093846, err = 0.028400\n",
      "\u001b[0m\n",
      "it 170/300, Jtr = 3.155029, err = 0.054250, \u001b[31m   time: 47.903640 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092095, err = 0.027700\n",
      "\u001b[0m\n",
      "it 171/300, Jtr = 3.149757, err = 0.054717, \u001b[31m   time: 48.037245 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096476, err = 0.027800\n",
      "\u001b[0m\n",
      "it 172/300, Jtr = 3.146425, err = 0.056117, \u001b[31m   time: 48.044948 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099888, err = 0.028200\n",
      "\u001b[0m\n",
      "it 173/300, Jtr = 3.134830, err = 0.054417, \u001b[31m   time: 47.901072 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090257, err = 0.027200\n",
      "\u001b[0m\n",
      "it 174/300, Jtr = 3.131213, err = 0.054333, \u001b[31m   time: 48.171069 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100233, err = 0.029700\n",
      "\u001b[0m\n",
      "it 175/300, Jtr = 3.124526, err = 0.055283, \u001b[31m   time: 48.015194 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.111595, err = 0.033700\n",
      "\u001b[0m\n",
      "it 176/300, Jtr = 3.117456, err = 0.054583, \u001b[31m   time: 48.090662 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095472, err = 0.029500\n",
      "\u001b[0m\n",
      "it 177/300, Jtr = 3.114742, err = 0.056050, \u001b[31m   time: 48.076152 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.116706, err = 0.036100\n",
      "\u001b[0m\n",
      "it 178/300, Jtr = 3.105188, err = 0.053800, \u001b[31m   time: 47.964780 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089189, err = 0.026300\n",
      "\u001b[0m\n",
      "it 179/300, Jtr = 3.098635, err = 0.053600, \u001b[31m   time: 47.946706 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095832, err = 0.029300\n",
      "\u001b[0m\n",
      "it 180/300, Jtr = 3.097203, err = 0.056417, \u001b[31m   time: 47.817505 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099067, err = 0.029600\n",
      "\u001b[0m\n",
      "it 181/300, Jtr = 3.088455, err = 0.055133, \u001b[31m   time: 47.867996 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088662, err = 0.024900\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 182/300, Jtr = 3.083828, err = 0.055783, \u001b[31m   time: 47.851843 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100676, err = 0.029700\n",
      "\u001b[0m\n",
      "it 183/300, Jtr = 3.076804, err = 0.055367, \u001b[31m   time: 47.954303 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099595, err = 0.031300\n",
      "\u001b[0m\n",
      "it 184/300, Jtr = 3.071350, err = 0.055017, \u001b[31m   time: 47.860656 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101890, err = 0.029200\n",
      "\u001b[0m\n",
      "it 185/300, Jtr = 3.063005, err = 0.053667, \u001b[31m   time: 47.949118 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095018, err = 0.028800\n",
      "\u001b[0m\n",
      "it 186/300, Jtr = 3.058478, err = 0.054650, \u001b[31m   time: 47.758690 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099471, err = 0.031800\n",
      "\u001b[0m\n",
      "it 187/300, Jtr = 3.052935, err = 0.055117, \u001b[31m   time: 47.905705 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104924, err = 0.030600\n",
      "\u001b[0m\n",
      "it 188/300, Jtr = 3.042579, err = 0.054000, \u001b[31m   time: 47.895059 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.126804, err = 0.041400\n",
      "\u001b[0m\n",
      "it 189/300, Jtr = 3.039824, err = 0.055767, \u001b[31m   time: 47.851174 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.115109, err = 0.036300\n",
      "\u001b[0m\n",
      "it 190/300, Jtr = 3.030191, err = 0.052883, \u001b[31m   time: 47.975903 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m    Jdev = 0.092481, err = 0.028300\n",
      "\u001b[0m\n",
      "it 191/300, Jtr = 3.029504, err = 0.055250, \u001b[31m   time: 47.867501 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098396, err = 0.030400\n",
      "\u001b[0m\n",
      "it 192/300, Jtr = 3.022014, err = 0.055300, \u001b[31m   time: 47.836429 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096777, err = 0.030200\n",
      "\u001b[0m\n",
      "it 193/300, Jtr = 3.010078, err = 0.053400, \u001b[31m   time: 47.971909 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103028, err = 0.033500\n",
      "\u001b[0m\n",
      "it 194/300, Jtr = 3.009339, err = 0.054617, \u001b[31m   time: 47.963317 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092614, err = 0.027400\n",
      "\u001b[0m\n",
      "it 195/300, Jtr = 3.005224, err = 0.055333, \u001b[31m   time: 47.848675 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097484, err = 0.029700\n",
      "\u001b[0m\n",
      "it 196/300, Jtr = 2.999284, err = 0.055467, \u001b[31m   time: 47.607212 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099509, err = 0.031900\n",
      "\u001b[0m\n",
      "it 197/300, Jtr = 2.990747, err = 0.054667, \u001b[31m   time: 47.640634 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097076, err = 0.029400\n",
      "\u001b[0m\n",
      "it 198/300, Jtr = 2.988328, err = 0.056067, \u001b[31m   time: 47.599146 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094169, err = 0.028600\n",
      "\u001b[0m\n",
      "it 199/300, Jtr = 2.978157, err = 0.054783, \u001b[31m   time: 47.697616 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104034, err = 0.032500\n",
      "\u001b[0m\n",
      "it 200/300, Jtr = 2.972043, err = 0.053367, \u001b[31m   time: 47.478993 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095232, err = 0.029200\n",
      "\u001b[0m\n",
      "it 201/300, Jtr = 2.967718, err = 0.055733, \u001b[31m   time: 47.546940 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.124697, err = 0.036700\n",
      "\u001b[0m\n",
      "it 202/300, Jtr = 2.965036, err = 0.055767, \u001b[31m   time: 47.563242 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096259, err = 0.030700\n",
      "\u001b[0m\n",
      "it 203/300, Jtr = 2.955485, err = 0.054833, \u001b[31m   time: 47.403349 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087902, err = 0.027100\n",
      "\u001b[0m\n",
      "it 204/300, Jtr = 2.950741, err = 0.054017, \u001b[31m   time: 47.432382 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095157, err = 0.028900\n",
      "\u001b[0m\n",
      "it 205/300, Jtr = 2.945771, err = 0.054917, \u001b[31m   time: 47.504772 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087613, err = 0.026300\n",
      "\u001b[0m\n",
      "it 206/300, Jtr = 2.938022, err = 0.054000, \u001b[31m   time: 47.892828 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092350, err = 0.027300\n",
      "\u001b[0m\n",
      "it 207/300, Jtr = 2.932217, err = 0.054183, \u001b[31m   time: 47.878070 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088854, err = 0.026800\n",
      "\u001b[0m\n",
      "it 208/300, Jtr = 2.928962, err = 0.054850, \u001b[31m   time: 47.900736 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085154, err = 0.024900\n",
      "\u001b[0m\n",
      "it 209/300, Jtr = 2.920175, err = 0.055033, \u001b[31m   time: 47.882664 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095292, err = 0.026100\n",
      "\u001b[0m\n",
      "it 210/300, Jtr = 2.916863, err = 0.054950, \u001b[31m   time: 47.618448 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100732, err = 0.030700\n",
      "\u001b[0m\n",
      "it 211/300, Jtr = 2.907981, err = 0.054033, \u001b[31m   time: 48.121839 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093346, err = 0.028200\n",
      "\u001b[0m\n",
      "it 212/300, Jtr = 2.899802, err = 0.053350, \u001b[31m   time: 48.132005 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088613, err = 0.027000\n",
      "\u001b[0m\n",
      "it 213/300, Jtr = 2.898388, err = 0.054750, \u001b[31m   time: 47.951118 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103462, err = 0.031700\n",
      "\u001b[0m\n",
      "it 214/300, Jtr = 2.894789, err = 0.055400, \u001b[31m   time: 47.924821 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096861, err = 0.029100\n",
      "\u001b[0m\n",
      "it 215/300, Jtr = 2.887121, err = 0.053300, \u001b[31m   time: 48.111796 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098746, err = 0.032000\n",
      "\u001b[0m\n",
      "it 216/300, Jtr = 2.881239, err = 0.053567, \u001b[31m   time: 47.983326 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088992, err = 0.028300\n",
      "\u001b[0m\n",
      "it 217/300, Jtr = 2.875846, err = 0.053883, \u001b[31m   time: 47.816216 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090830, err = 0.026700\n",
      "\u001b[0m\n",
      "it 218/300, Jtr = 2.869893, err = 0.054600, \u001b[31m   time: 43.932264 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095008, err = 0.029300\n",
      "\u001b[0m\n",
      "it 219/300, Jtr = 2.865658, err = 0.054217, \u001b[31m   time: 48.018942 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092833, err = 0.026500\n",
      "\u001b[0m\n",
      "it 220/300, Jtr = 2.862025, err = 0.055717, \u001b[31m   time: 47.919553 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096666, err = 0.027800\n",
      "\u001b[0m\n",
      "it 221/300, Jtr = 2.852477, err = 0.053383, \u001b[31m   time: 48.066621 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089161, err = 0.025600\n",
      "\u001b[0m\n",
      "it 222/300, Jtr = 2.848048, err = 0.054900, \u001b[31m   time: 48.210441 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099980, err = 0.030000\n",
      "\u001b[0m\n",
      "it 223/300, Jtr = 2.846106, err = 0.054833, \u001b[31m   time: 47.868849 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.109846, err = 0.032300\n",
      "\u001b[0m\n",
      "it 224/300, Jtr = 2.836394, err = 0.053367, \u001b[31m   time: 47.879002 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095413, err = 0.029500\n",
      "\u001b[0m\n",
      "it 225/300, Jtr = 2.829961, err = 0.054517, \u001b[31m   time: 47.855696 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090737, err = 0.027600\n",
      "\u001b[0m\n",
      "it 226/300, Jtr = 2.821819, err = 0.053483, \u001b[31m   time: 47.926472 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089970, err = 0.027900\n",
      "\u001b[0m\n",
      "it 227/300, Jtr = 2.819966, err = 0.054650, \u001b[31m   time: 47.870694 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.113159, err = 0.034700\n",
      "\u001b[0m\n",
      "it 228/300, Jtr = 2.815366, err = 0.053750, \u001b[31m   time: 47.884047 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107658, err = 0.033200\n",
      "\u001b[0m\n",
      "it 229/300, Jtr = 2.809535, err = 0.054033, \u001b[31m   time: 47.851677 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103427, err = 0.034100\n",
      "\u001b[0m\n",
      "it 230/300, Jtr = 2.803038, err = 0.054067, \u001b[31m   time: 47.991213 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096263, err = 0.029100\n",
      "\u001b[0m\n",
      "it 231/300, Jtr = 2.796826, err = 0.053550, \u001b[31m   time: 47.750945 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095002, err = 0.028300\n",
      "\u001b[0m\n",
      "it 232/300, Jtr = 2.793994, err = 0.053833, \u001b[31m   time: 47.553900 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092872, err = 0.028200\n",
      "\u001b[0m\n",
      "it 233/300, Jtr = 2.784809, err = 0.052633, \u001b[31m   time: 47.464246 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089892, err = 0.026600\n",
      "\u001b[0m\n",
      "it 234/300, Jtr = 2.784289, err = 0.055467, \u001b[31m   time: 47.485017 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095771, err = 0.029600\n",
      "\u001b[0m\n",
      "it 235/300, Jtr = 2.774766, err = 0.052817, \u001b[31m   time: 47.479258 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090808, err = 0.027900\n",
      "\u001b[0m\n",
      "it 236/300, Jtr = 2.770028, err = 0.053300, \u001b[31m   time: 47.488667 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098758, err = 0.030700\n",
      "\u001b[0m\n",
      "it 237/300, Jtr = 2.765744, err = 0.053717, \u001b[31m   time: 47.397299 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089533, err = 0.027100\n",
      "\u001b[0m\n",
      "it 238/300, Jtr = 2.758412, err = 0.053350, \u001b[31m   time: 47.353067 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096400, err = 0.030600\n",
      "\u001b[0m\n",
      "it 239/300, Jtr = 2.753598, err = 0.054233, \u001b[31m   time: 47.460659 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105394, err = 0.031700\n",
      "\u001b[0m\n",
      "it 240/300, Jtr = 2.754738, err = 0.056500, \u001b[31m   time: 48.169273 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091896, err = 0.028400\n",
      "\u001b[0m\n",
      "it 241/300, Jtr = 2.745140, err = 0.054150, \u001b[31m   time: 47.956902 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.100095, err = 0.031500\n",
      "\u001b[0m\n",
      "it 242/300, Jtr = 2.739515, err = 0.053933, \u001b[31m   time: 47.872305 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094715, err = 0.027600\n",
      "\u001b[0m\n",
      "it 243/300, Jtr = 2.728992, err = 0.053017, \u001b[31m   time: 47.810942 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096468, err = 0.029600\n",
      "\u001b[0m\n",
      "it 244/300, Jtr = 2.725918, err = 0.053200, \u001b[31m   time: 47.885909 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092886, err = 0.030300\n",
      "\u001b[0m\n",
      "it 245/300, Jtr = 2.720864, err = 0.053383, \u001b[31m   time: 47.757075 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096204, err = 0.030700\n",
      "\u001b[0m\n",
      "it 246/300, Jtr = 2.712726, err = 0.053300, \u001b[31m   time: 47.896264 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107526, err = 0.034500\n",
      "\u001b[0m\n",
      "it 247/300, Jtr = 2.711328, err = 0.054200, \u001b[31m   time: 47.953033 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089116, err = 0.027700\n",
      "\u001b[0m\n",
      "it 248/300, Jtr = 2.700354, err = 0.052750, \u001b[31m   time: 47.805355 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091927, err = 0.027700\n",
      "\u001b[0m\n",
      "it 249/300, Jtr = 2.701438, err = 0.053967, \u001b[31m   time: 47.842403 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092624, err = 0.029500\n",
      "\u001b[0m\n",
      "it 250/300, Jtr = 2.695534, err = 0.054000, \u001b[31m   time: 47.776356 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095066, err = 0.027400\n",
      "\u001b[0m\n",
      "it 251/300, Jtr = 2.686839, err = 0.052867, \u001b[31m   time: 47.888296 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091045, err = 0.027300\n",
      "\u001b[0m\n",
      "it 252/300, Jtr = 2.682622, err = 0.053300, \u001b[31m   time: 47.886558 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.110063, err = 0.033500\n",
      "\u001b[0m\n",
      "it 253/300, Jtr = 2.681515, err = 0.055700, \u001b[31m   time: 47.575772 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108685, err = 0.034000\n",
      "\u001b[0m\n",
      "it 254/300, Jtr = 2.675264, err = 0.054667, \u001b[31m   time: 47.479659 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092805, err = 0.028000\n",
      "\u001b[0m\n",
      "it 255/300, Jtr = 2.668446, err = 0.053767, \u001b[31m   time: 47.331381 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m    Jdev = 0.091067, err = 0.028300\n",
      "\u001b[0m\n",
      "it 256/300, Jtr = 2.661860, err = 0.053233, \u001b[31m   time: 47.577457 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093269, err = 0.027200\n",
      "\u001b[0m\n",
      "it 257/300, Jtr = 2.657432, err = 0.054417, \u001b[31m   time: 47.468588 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103213, err = 0.030700\n",
      "\u001b[0m\n",
      "it 258/300, Jtr = 2.651628, err = 0.053500, \u001b[31m   time: 47.603098 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108040, err = 0.033300\n",
      "\u001b[0m\n",
      "it 259/300, Jtr = 2.650985, err = 0.054800, \u001b[31m   time: 47.617105 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094068, err = 0.029200\n",
      "\u001b[0m\n",
      "it 260/300, Jtr = 2.641342, err = 0.053383, \u001b[31m   time: 47.426616 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091467, err = 0.026500\n",
      "\u001b[0m\n",
      "it 261/300, Jtr = 2.636270, err = 0.053317, \u001b[31m   time: 47.550840 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093939, err = 0.028400\n",
      "\u001b[0m\n",
      "it 262/300, Jtr = 2.627447, err = 0.053267, \u001b[31m   time: 47.850166 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099385, err = 0.029200\n",
      "\u001b[0m\n",
      "it 263/300, Jtr = 2.628004, err = 0.053667, \u001b[31m   time: 47.760784 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097178, err = 0.031500\n",
      "\u001b[0m\n",
      "it 264/300, Jtr = 2.621418, err = 0.053950, \u001b[31m   time: 47.889065 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104990, err = 0.032600\n",
      "\u001b[0m\n",
      "it 265/300, Jtr = 2.617447, err = 0.054550, \u001b[31m   time: 47.933126 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.106702, err = 0.032700\n",
      "\u001b[0m\n",
      "it 266/300, Jtr = 2.611193, err = 0.054450, \u001b[31m   time: 47.980686 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103477, err = 0.033100\n",
      "\u001b[0m\n",
      "it 267/300, Jtr = 2.606632, err = 0.053550, \u001b[31m   time: 48.097803 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085358, err = 0.025500\n",
      "\u001b[0m\n",
      "it 268/300, Jtr = 2.598716, err = 0.053583, \u001b[31m   time: 48.012179 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091461, err = 0.029300\n",
      "\u001b[0m\n",
      "it 269/300, Jtr = 2.596286, err = 0.053983, \u001b[31m   time: 47.618785 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103615, err = 0.032200\n",
      "\u001b[0m\n",
      "it 270/300, Jtr = 2.590713, err = 0.054400, \u001b[31m   time: 47.636921 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091036, err = 0.027100\n",
      "\u001b[0m\n",
      "it 271/300, Jtr = 2.584920, err = 0.052550, \u001b[31m   time: 47.507646 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086762, err = 0.025900\n",
      "\u001b[0m\n",
      "it 272/300, Jtr = 2.579605, err = 0.053417, \u001b[31m   time: 47.601011 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092368, err = 0.027500\n",
      "\u001b[0m\n",
      "it 273/300, Jtr = 2.576670, err = 0.053367, \u001b[31m   time: 47.389416 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105643, err = 0.031300\n",
      "\u001b[0m\n",
      "it 274/300, Jtr = 2.570284, err = 0.053450, \u001b[31m   time: 47.468027 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108662, err = 0.033100\n",
      "\u001b[0m\n",
      "it 275/300, Jtr = 2.569432, err = 0.055200, \u001b[31m   time: 47.381761 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096568, err = 0.030200\n",
      "\u001b[0m\n",
      "it 276/300, Jtr = 2.561168, err = 0.053433, \u001b[31m   time: 47.782332 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090010, err = 0.028000\n",
      "\u001b[0m\n",
      "it 277/300, Jtr = 2.553636, err = 0.053183, \u001b[31m   time: 47.604053 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094878, err = 0.029500\n",
      "\u001b[0m\n",
      "it 278/300, Jtr = 2.553203, err = 0.053067, \u001b[31m   time: 47.514008 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.103335, err = 0.032300\n",
      "\u001b[0m\n",
      "it 279/300, Jtr = 2.545198, err = 0.052650, \u001b[31m   time: 47.507365 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090104, err = 0.026000\n",
      "\u001b[0m\n",
      "it 280/300, Jtr = 2.540864, err = 0.053833, \u001b[31m   time: 47.588608 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086890, err = 0.026900\n",
      "\u001b[0m\n",
      "it 281/300, Jtr = 2.536013, err = 0.053183, \u001b[31m   time: 47.407796 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087795, err = 0.026800\n",
      "\u001b[0m\n",
      "it 282/300, Jtr = 2.530153, err = 0.053283, \u001b[31m   time: 47.455489 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092165, err = 0.028100\n",
      "\u001b[0m\n",
      "it 283/300, Jtr = 2.526504, err = 0.053867, \u001b[31m   time: 47.794371 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093059, err = 0.027800\n",
      "\u001b[0m\n",
      "it 284/300, Jtr = 2.516421, err = 0.051667, \u001b[31m   time: 47.966334 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091554, err = 0.029000\n",
      "\u001b[0m\n",
      "it 285/300, Jtr = 2.516914, err = 0.053700, \u001b[31m   time: 47.796768 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104019, err = 0.030400\n",
      "\u001b[0m\n",
      "it 286/300, Jtr = 2.512037, err = 0.052233, \u001b[31m   time: 47.496522 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093395, err = 0.028100\n",
      "\u001b[0m\n",
      "it 287/300, Jtr = 2.505129, err = 0.054217, \u001b[31m   time: 47.685690 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090779, err = 0.027200\n",
      "\u001b[0m\n",
      "it 288/300, Jtr = 2.501544, err = 0.053083, \u001b[31m   time: 47.951787 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098568, err = 0.028600\n",
      "\u001b[0m\n",
      "it 289/300, Jtr = 2.494538, err = 0.052867, \u001b[31m   time: 48.129006 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.102279, err = 0.032300\n",
      "\u001b[0m\n",
      "it 290/300, Jtr = 2.492842, err = 0.052967, \u001b[31m   time: 48.081074 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095713, err = 0.029900\n",
      "\u001b[0m\n",
      "it 291/300, Jtr = 2.486988, err = 0.054367, \u001b[31m   time: 47.971619 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086895, err = 0.025600\n",
      "\u001b[0m\n",
      "it 292/300, Jtr = 2.479227, err = 0.052983, \u001b[31m   time: 47.739686 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094061, err = 0.028600\n",
      "\u001b[0m\n",
      "it 293/300, Jtr = 2.474061, err = 0.052083, \u001b[31m   time: 47.807797 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098005, err = 0.029700\n",
      "\u001b[0m\n",
      "it 294/300, Jtr = 2.473863, err = 0.054933, \u001b[31m   time: 47.894148 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091830, err = 0.029200\n",
      "\u001b[0m\n",
      "it 295/300, Jtr = 2.464068, err = 0.052267, \u001b[31m   time: 47.921835 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.110598, err = 0.033900\n",
      "\u001b[0m\n",
      "it 296/300, Jtr = 2.457148, err = 0.052483, \u001b[31m   time: 48.052807 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.113200, err = 0.036700\n",
      "\u001b[0m\n",
      "it 297/300, Jtr = 2.459062, err = 0.055100, \u001b[31m   time: 47.980064 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105148, err = 0.033200\n",
      "\u001b[0m\n",
      "it 298/300, Jtr = 2.449495, err = 0.051950, \u001b[31m   time: 48.061309 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084032, err = 0.026000\n",
      "\u001b[0m\n",
      "it 299/300, Jtr = 2.446837, err = 0.053167, \u001b[31m   time: 48.076758 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092462, err = 0.028500\n",
      "\u001b[0m\n",
      "\u001b[31m   average time: 47.281130 seconds\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_importance/theta_last.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "RESULTS:\u001b[0m\n",
      "  cost_dev: 0.084032 (cost_train 2.446837)\n",
      "  err_dev: 0.024900\n",
      "  nb_parameters: 4790420 (4.57MB)\n",
      "  time_per_it: 47.281130s\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "\n",
    "models_dir = 'models_weight_uncertainty_MC_MNIST_importance'\n",
    "results_dir = 'results_weight_uncertainty_MC_MNIST_importance'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 300\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [20, 50, 80, 120]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "nsamples = 1 # We use a single montecarlo sample for expectation but this number refers to k from inner sum\n",
    "########################################################################################\n",
    "net = Bayes_Net_IW(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,\n",
    "          Nbatches=(NTrainPointsMNIST/batch_size))\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "# kl_cost_train = np.zeros(nb_epochs)\n",
    "# pred_cost_train = np.zeros(nb_epochs)\n",
    "cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost, err = net.fit(x, y, samples=nsamples)\n",
    "\n",
    "        err_train[i] += err\n",
    "        cost_train[i] += cost\n",
    "#         kl_cost_train[i] += cost_dkl\n",
    "#         pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "#     kl_cost_train[i] /= nb_samples\n",
    "    cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr = %f, err = %f, \" % (i, nb_epochs, cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "\n",
    "    if i == 10:\n",
    "        nsamples = 5\n",
    "        print('now running IW with %d samples' % nsamples)\n",
    "    \n",
    "    # Save state dict\n",
    "    if i in savemodel_its:\n",
    "        save_dicts.append(copy.deepcopy(net.model.state_dict()))\n",
    "    \n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "            cprint('b', 'best test error')\n",
    "#             net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "net.save(models_dir+'/theta_last.dat')\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', cost_train)\n",
    "# np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('-E log like')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "plt.figure()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(cost_train, 'r')\n",
    "ax1.set_ylabel('nats?')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax = plt.gca()\n",
    "plt.title('DKL (per sample)')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('-E log like')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# plt.figure()\n",
    "# fig, ax1 = plt.subplots()\n",
    "# ax1.plot(kl_cost_train, 'r')\n",
    "# ax1.set_ylabel('nats?')\n",
    "# plt.xlabel('epoch')\n",
    "# plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "# plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "# ax = plt.gca()\n",
    "# plt.title('DKL (per sample)')\n",
    "# for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "#     ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "#     item.set_fontsize(textsize)\n",
    "#     item.set_weight('normal')\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Draw samples from the reweighed approx posterior for inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drawing sample 0\n",
      "drawing sample 1\n",
      "drawing sample 2\n",
      "drawing sample 3\n",
      "drawing sample 4\n",
      "drawing sample 5\n",
      "drawing sample 6\n",
      "drawing sample 7\n",
      "drawing sample 8\n",
      "drawing sample 9\n",
      "drawing sample 10\n",
      "drawing sample 11\n",
      "drawing sample 12\n",
      "drawing sample 13\n",
      "drawing sample 14\n",
      "drawing sample 15\n",
      "drawing sample 16\n",
      "drawing sample 17\n",
      "drawing sample 18\n",
      "drawing sample 19\n",
      "drawing sample 20\n",
      "drawing sample 21\n",
      "drawing sample 22\n",
      "drawing sample 23\n",
      "drawing sample 24\n",
      "drawing sample 25\n",
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      "drawing sample 27\n",
      "drawing sample 28\n",
      "drawing sample 29\n",
      "drawing sample 30\n",
      "drawing sample 31\n",
      "drawing sample 32\n",
      "drawing sample 33\n",
      "drawing sample 34\n",
      "drawing sample 35\n",
      "drawing sample 36\n",
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      "drawing sample 39\n",
      "drawing sample 40\n",
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      "drawing sample 44\n",
      "drawing sample 45\n",
      "drawing sample 46\n",
      "drawing sample 47\n",
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      "drawing sample 50\n",
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      "drawing sample 162\n",
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      "drawing sample 164\n",
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      "drawing sample 166\n",
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      "drawing sample 168\n",
      "drawing sample 169\n",
      "drawing sample 170\n",
      "drawing sample 171\n",
      "drawing sample 172\n",
      "drawing sample 173\n",
      "drawing sample 174\n",
      "drawing sample 175\n",
      "drawing sample 176\n",
      "drawing sample 177\n",
      "drawing sample 178\n",
      "drawing sample 179\n",
      "drawing sample 180\n",
      "drawing sample 181\n",
      "drawing sample 182\n",
      "drawing sample 183\n",
      "drawing sample 184\n",
      "drawing sample 185\n",
      "drawing sample 186\n",
      "drawing sample 187\n",
      "drawing sample 188\n",
      "drawing sample 189\n",
      "drawing sample 190\n",
      "drawing sample 191\n",
      "drawing sample 192\n",
      "drawing sample 193\n",
      "drawing sample 194\n",
      "drawing sample 195\n",
      "drawing sample 196\n",
      "drawing sample 197\n",
      "drawing sample 198\n",
      "drawing sample 199\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "sample_dict_vector = net.draw_weighed_samples(trainloader, Nsamples=300, k=5)\n",
    "\n",
    "\n",
    "with open(results_dir+'/sample_dict_vector.pickle', 'wb') as handle:\n",
    "    pickle.dump(sample_dict_vector, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# with open('filename.pickle', 'rb') as handle:\n",
    "#     b = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  8., 11., 23., 28., 47., 31., 21., 20., 10.]),\n",
       " array([-0.24898723, -0.20759736, -0.16620749, -0.12481763, -0.08342776,\n",
       "        -0.0420379 , -0.00064803,  0.04074183,  0.0821317 ,  0.12352157,\n",
       "         0.16491143], dtype=float32),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = []\n",
    "for a in sample_dict_vector:\n",
    "    values.append(a['bfc3.W'][6,7].cpu().numpy())\n",
    "    \n",
    "print(len(values))\n",
    "plt.hist(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot distribution of individual parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inference with sampling on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Jtest = 0.112726, err = 0.027200\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, sample_dict_vector, logits=False) # , logits=True\n",
    "    \n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "test_cost /= nb_samples\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for single digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:83: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind = 90\n",
    "Nsamples = len(sample_dict_vector)\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 8), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), sample_dict_vector, logits=False) # , logits=True\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('rotation angle')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All dataset with entropy\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "net.set_mode_train(False)\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), sample_dict_vector, logits=False) # , logits=True\n",
    "\n",
    "    predictions = probs.numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "          \n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('rotation angle')\n",
    "lgd = plt.legend(['correct class', 'posterior predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_weight_samples(sample_dict_vector):\n",
    "        weight_vec = []\n",
    "        for i, sample_dict in enumerate(sample_dict_vector):\n",
    "            previous_layer_name = ''\n",
    "            for key in sample_dict.keys():\n",
    "                layer_name = key.split('.')[0]\n",
    "                if layer_name != previous_layer_name:\n",
    "                    previous_layer_name = layer_name\n",
    "\n",
    "                    W = sample_dict[layer_name+'.W']\n",
    "#                     b = sample_dict[layer_name+'.b']\n",
    "\n",
    "                    for weight in W.cpu().view(-1):\n",
    "                        weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43070400,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 4307040, samples: 10')"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "Nsamples = 10\n",
    "weight_vector = get_weight_samples(sample_dict_vector[1:10])\n",
    "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "symlim = 0.7\n",
    "\n",
    "lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "\n",
    "sns.distplot(weight_vector, 500, norm_hist=False, label=name, ax=ax)\n",
    "ax.set_xlim((-symlim, symlim))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d, samples: %d' % (len(weight_vector)/Nsamples, Nsamples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution over iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2PR2NsrwtwUrhd8VK0rFhJlAAqA4EOwA1we+K7ekobiux5YJLngQnZgBAnBHsAFS9bC6/sJBwb0fpEyekpRMwhljLDkCVINgBqHpD47PKO44kqac9nBa7+sBYvaFxgh2A6kCwA1D1jgdmxIbVFSstLnQ8MjEX2jkBoJwIdgCqXnANu56QumIlqbvNXTZlkK5YAFWCYAeg6vkTJ1JJS+0tpa9h5+v0WuyGxmbkeF29ABBnBDsAVc8Pdt3tjUqEsNSJz++KncvkWcsOQFUg2AGoev5yJMElSsIQ3MGCCRQAqgHBDkBVS6YSi8EuxIkT0mKLncSSJwCqA8EOQFUbm5zXfDYvaXF/17B0tgVb7JgZCyD+CHYAqtrAyOJ2X2HOiJWklsa00kn3Y5IWOwDVgGAHoKoFlyIJdp2GwbIstbfWSWKMHYDqQLADUNWCLWmdIQc7Serwlk+hxQ5ANSDYAahqfotdU0NK9elk6Of318WjxQ5ANSDYAahqQ2PujNjg0iRh2tLVJEmanMkoxxrFAGKOYAegqvktaeUKdt2B7t1hWu0AxBzBDkDVchxnYexbR5mCXVdgyRP2jAUQdwQ7AFVrei6r2fmcpMVJDmELTsgY9Lp9ASCuCHYAqtaSGbGt4c+IldzJE/72s4OjtNgBiDeCHYCqNTadWbhdrq7YZMJSa5O3lh0tdgBijmAHoGoFJzOUa/KEtLjkCWPsAMQdwQ5A1fK7YhMJSy1N6bK9Tnuz22LHGDsAcUewA1C1/Ba0tuY6JfyBcGXgt9iNTMwpm8uX7XUAoFQEOwBVy2+x81vUyqW9xT2/40ijE3NlfS0AKEUqzJMZYxKSbpF0oaSbbdt+UZjnB4CgoXG3a7St3MGueXH83tD4rHo6Gsv6egBQrLBb7N4l6ckhnxMATpDN5TXitZ4Fg1c5BFsERyfny/paAFCK0IKdMeYUSX8h6UNhnRMAVjI6OSfH27u1raUyXbGSNMi2YgBiLMwWu89Juk/StSGeEwAKGh5fHOtW7jF26VRSzY3urFuWPAEQZ6EEO2PMWyQ9V9JbbNtmyhiAshsKtJyVuytWWlwnb4hgByDGSp48YYzZLuljkj5u2/bdpRfpRJ2dTeU4bdVKJt08Tr0sRb0UVqv1MpNZ/Buyq6NB6XRSiYS75Ek6nVj1e8uSLGndx6fTCXW3N+jwwKSGx2drri59tXqtlIp6KYx6iacwWuz+XtKQpD8P4VwAsC7HR90ZsU0NKdWnk2V/vc42dy/awdEZOf7gPgCImZJa7Iwxvy7pckmX2rZdtiXZR0amy3XqquT/dUS9LEW9FFar9XJscEqS1NnaoEwmr0wmJ8sLeGt97ziSIymfd9Z1fCaTV6s3xm4+m9fho+NqaSzfThdRqdVrpVTUS2HUy4l6e1ujLkLxwc4YUy/pbyV9X9JBY8xpyw5p9O6bsG27v4QyAsAJ/DF2nW3lH18nLZ2gMTIxV5PBDkD1K6UrtlFSr6QXS3po2T9JepZ3++OlFBAACvEnMXS1VijYtSwNdgAQR6V0xU5Jet0Kj31F0j5JH5Z0qITXAIATTM9mNTOXleR2xVaCv1+s5K6hBwBxVHSws207I+mrhR4zxkjSgG3bBR8HgFIMB5Y6qVRXbFszLXYA4i/sLcUAoOyCa9h1VqgrNpVMqLnB/VuYYAcgrkpex64Q27atcpwXAKRlLXatDZrPVGZd9I7Wek3NZjU2xX6xAOKJFjsAVWfI204skbCWdJGWW4c3zo4WOwBxRbADUHX8FruOlrqF3SEqoX0h2LGtGIB4ItgBqDoLa9hVaEasz1/yZGI6o0yWbbEBxA/BDkDVWQx2lZk44etgyRMAMUewA1BV8nlHoxPu5IWOCge74Fp2jLMDEEcEOwBVZWY+p7zj7vFa6WDXwe4TAGKOYAegqowEukCDXaOVQIsdgLgj2AGoKsFA1V7BpU4kqakhpVTSnYXLGDsAcUSwA1BVlgS7CrfYWdbiunm02AGII4IdgKriByrLklor3GInSa1NXrCjxQ5ADBHsAFQVf3Hg1qY6JSu4OLHPb7EbpcUOQAwR7ABUlRFvO7FKbiUWtBDsJufkeLNzASAuCHYAqorfBVrpiRO+3o5GSVI252g2w+4TAOKFYAegqox4u060tUQT7DrbFrcxY2YsgLgh2AGoGvOZnKZms5Kia7ELLlI8PE6wAxAvBDsAVSPYQtbWXNmlTnwsUgwgzgh2AKrG6OT8wu2oWuyCr+vP0AWAuCDYAaga49OZhdtRjbFLJhNqbkhLkoZpsQMQMwQ7AFUj2BUbVYudJLU2u8HOn8gBAHFBsANQNfwxbamkpcb6VGTlWNh9ghY7ADFDsANQNfwg1dpUJ8uq/K4TPoIdgLgi2AGoGv5khZam6LphJam1ye2KnZzJaD6Ti7QsABBEsANQNRZb7NKRlqM1ECxHWKQYQIwQ7ABUBcdxlnTFRin4+qN0xwKIEYIdgKowNZtVJuvuzRp1i11b4PWDS7AAQNQIdgCqQnCpk9bG+LTY0RULIE4IdgCqQrDLszXCNewkqb4uqXTK/fhkZiyAOCHYAagKwQAVdVesZVlq93a+GBkn2AGID4IdgKoQ7IptibgrVpLam+slsV8sgHgh2AGoCiOT85KkpobUQjdolPwWO/aLBRAn0X86AsA6jE+5wc5vKYtamzfOb3RiTnnHibg0AOAi2AGoCv4YO7+lLGrtLW7AzOUdTbDkCYCYINgBqAqLwS4eLXYdzSxSDCB+CHYAYi+Xz2tsyg1PHTFpsWsLBEyWPAEQFwQ7ALE3NjkvfxhbW0xa7IJdwixSDCAuCHYAYm/UmxErxafFrrWpTpbl3qbFDkBcEOwAxF4wOMVljF0yYaml0V0omTF2AOKCYAcg9oKLE7dHvJ1YUEerGzLHpubXOBIAKoNgByD2/GCXsNwu0LjoaGH3CQDxQrADEHt+V2drc50SCSvi0izyu4XZfQJAXBDsAMSeP+s0LrtO+PyJHNOzWc3N5yIuDQAQ7ABUgbjtOuELTuRgyRMAcUCwAxB7/nIncZkR6+tgkWIAMUOwAxBrc/M5zcxlJcWxxY5txQDEC8EOQKwFlzrpiFmLHV2xAOKGYAcg1oJdnHELdo31KdWl3Y9RumIBxAHBDkCsBVvs2mLWFStJbd66enTFAogDgh2AWBuJcVes5K6tJ9EVCyAeCHYAYs3v4qxLJdRQl4y4NCfyW+zoigUQBwQ7ALEWXOrEsuKz64SvzWuxG5ucVz7vRFwaAJsdwQ5ArPlj1/wAFTd9XU2SpLzjaHx6PuLSANjsCHYAYs2fPNERw4kTktTZyiLFAOKDYAcgthzHWQh2bTGcOCGxSDGAeCHYAYityZmMsjl33Fp7TLtiO1ikGECMpIp9ojGmR9JfS3qapJ2SmiU9Iennkv6vbdt3h1JCAJtWnBcn9rU21SlhSXmHrlgA0Sulxa5D0pmSbpL055LeJunfJF0s6Q5jzKWlFw/AZjYxk1m43d4az2CXSFhqYZFiADFRdIudbdsPS3rW8vuNMZ+W9JikP5T038UXDcBm5y91IsW3xU6SWpvSGp+apysWQOTKMcauX9K03BY9AChasGszrsudSItloysWQNSKbrHzGWPSktq9c+2S9F5JrZJuLPXcADY3Pyg1N6SUTsV3rhe7TwCIi5KDnaRnS/pB4PsxSX8l6cMhnFuS1NnZFNapakIy6f4HR70sRb0UVs31MjG9uOtEIrG460Q6nVA6nVy4b6PfW5ZkSUU/f/n3Hd74v9n5nBqa6tRYH8ZHa+VV87VSTtRLYdRLPIXx6XO3pEsl1Us6Q9KVclvs6iVlQzg/gE1qeHxWkhvs4izYTTw0NqudfS0RlgbAZlZysLNte0TuzFhJ+rYx5gtyw94pkl5W6vklaWRkOozT1Az/ryPqZSnqpbBqrpfBMTfYtTall+zDmsnklcnkZKWTRX3vOJIjLZyz1PM1N6QXynboyKia0/HtNl5NNV8r5US9FEa9nKi3tzXqIoQ/ecILet+S9FJjzJ6wzw9gc8jm8hqf8rpiYzxxQpK29TQv3J6YzqxyJACUV7n+rGz0vnaW6fwAatxYYKmTuHfFdrU1LNxmyRMAUSo62Bljtqxw/x5Jr5Y7ieKBYs8PYHMLBqQ4L3UiSfV1SdV73bLMjAUQpVLG2P2ht7vEdyQdlDtk5UmS3iCpRdIbbdueLbmEADal4C4O7S3xDnaSOw5wbiyn4XGCHYDolBLsbpS0Q9JrJfV55zrq3X+Nbdu3l148AJtVNbXYSe6esYNjsxqZ4O9ZANEpZUuxm7Q4GxYAQjXqBbuEZam5Mb3G0dFrZZFiADFQnXPyAdQ8vyu2pTGthGWtcXT0Wr1WxdHJOeXy+YhLA2CzItgBiKVRb6mT1ub4t9ZJUluTW07HkcanWPIEQDQIdgBiyW+x87s44y5YTrpjAUSFYAcglkYIdgCwYQQ7ALEzM5fV7HxOkruMSDUIlnOURYoBRIRgByB2gsGopUpa7JoDkzxosQMQFYIdgNgJLk5cLV2xlmWpxVuWhWAHICoEOwCxMxwMdlWwhp2vo9Xd03Zsan6NIwGgPAh2AGInGOyqYdcJn7/12TC7TwCICMEOQOyMjLvBqKk+pbp0MuLSrJ8f7EbG5+Q4TsSlAbAZEewAxI7fYud3bVaL9ma3vHOZnGbmchGXBsBmRLADEDvDXotd1QW7lsBadix5AiACBDsAsTM87rXYtVRXsAuOBxxlZiyACBDsAMTK7HxW03NZSVJnW3UFu/ZAEGXJEwBRINgBiBW/tU6SOlsbIizJxrU30xULIFoEOwCxEmzp6qyyMXZ16aQa6txZvHTFAogCwQ5ArPgTJ6TqmzwhLY6zoysWQBQIdgBiJbg4cbVNnpAWt0CjKxZAFAh2AGLFb7FraUornaq+jyh/nB1dsQCiUH2fmgBqmt9i11mFrXWS1NPZKEkan5pXNpePuDQANhuCHYBY8Vvs2qs02PkTPhy54Q4AKolgByA2HMdZbLGrwokTEmvZAYgWwQ5AbMzMZTU37+6xWo0zYqVl24oR7ABUGMEOQGwEFyeuxhmx0tJyMzMWQKUR7ADExvDE4hp21doV29yYVjJhSWJmLIDKI9gBiI2xqczC7c726tpOzJewLLU2pSXRYgeg8gh2AGLDnxFrSeoI7LtabVr93SfGCXYAKotgByA2/Bmxrc11Siar9+Opjd0nAESkej85AdScoTG3xa5aJ0742gK7TziOE3FpAGwmBDsAseF3xXa0Vm83rCS1NbvBdD6b19RsNuLSANhMCHYAYsFxHA2N10aLXTCY+q2QAFAJBDsAsTA5k1Em6+6tWu3Bbntvy8LtUbYVA1BBBDsAsRBcnLi9Stew8/UElmoZpMUOQAUR7ADEQnBx4mpvsWtpTCvlzeodHJuJuDQANhOCHYBYWLqdWHVPnrAsa2HP2MFRWuwAVA7BDkAs+C127s4N1R3spMVWR1rsAFQSwQ5ALIxOupMMWprSSnh7rVazdm8tO2bFAqgkgh2AWPDXsGur4q3Egtq9FrvJmYxm5ljLDkBlEOwAxELtBbvAWnbjtNoBqAyCHYDI5R1nYfJEe60Eu+bFmb10xwKoFIIdgMiNTc4rl3f3VA0GomrWHliyhRY7AJVCsAMQuWCLVnuVL3Xia2lcnARCix2ASiHYAYjc4PjikiDtVb44sS+RsBaWPBmemFvjaAAIB8EOQOSWttjVRrCTpM42fy07WuwAVAbBDkDk/GDXVJ9SfToZcWnC09nKIsUAKotgByByg97kgq62hohLEi4/2I1NziuTzUVcGgCbAcEOQOT8Fju/67JWdLYuBtXgXrgAUC4EOwCRchxnIdh1tddWi11XIKgOsuQJgAog2AGI1MR0RvPZvKRa7IpdfD8seQKgEgh2ACIVXLy3q7W2gl17S50s7zYzYwFUAsEOQKSCLVld7bU1xi6VTKjV2yKNFjsAlUCwAxCpYEtWrbXYSYs7abCtGIBKINgBiJTfklVfl1RTQyri0oSvt6NRkjRMsANQAQQ7AJHyF+/taq2XZVlrHF19ur2ZvsPjc8rl8xGXBkCtK/rPY2PM6ZJeL+lSSadKapV0SNJNkv7Stu2joZQQQE0bqtHFiX08K81wAAAgAElEQVT++8o7jkYn5heCHgCUQyktdldJer+kJyT9X0nvlnSbpKsl3WuMObP04gGoZY7jLAS7zhoNdsElT9haDEC5lTKg5auS/sq27ZHAfZ81xtwm6TOSPizp10opHIDaNj2X1cycu9VWV2ttzYj1BRcpZgIFgHIrOtjZtr13hYe+JDfYnVvsuQFsDkuWOqnVFrs2FikGUDnlmDyxw/s6UIZzA6ghI5PzC7drbTsxX316cbYvLXYAyq0cawt8xPv6T2GdsLOzKaxT1YRk0s3j1MtS1Ethca6XybljC7d7OxqVSFhKpxNKp5NKJNwZssu/L3TfRr+3LMmSin7+RsvU2Vqv6dmsRqcysfw5+OJ8rUSJeimMeomnUFvsjDF/JOk1kq6X9M9hnhtA7Tk+4k4mSKcSamlKR1ya8vFbI/uHpyMuCYBaF1qLnTHmnZL+j6QfSnq9bdtOWOceGeHDMMj/64h6WYp6KSzO9fLE8SlJUkdLvRzHnSWbyeSVyeRkpZOSdML3he7b6PeOIzmS8nknlPOtdUxHizuB4vjIjI4PTiqVjOcSonG+VqJEvRRGvZyot7c16iKE02JnjHmPpE9KulnSy23b5qcMYE0LixO31eaMWJ+/VVo+sLwLAJRDycHOGPN+SR+X9F1JryDUAVivwVFvDbsa3CM2KBhcj4+ylh2A8ikp2Blj/lDSX0m6UdKrbdvmT1EA6zI7n9XkTEaS1FnjLXYnbVnsnhkan4uwJABqXSlbil0t6aOS+iV9XdLrjDHBQyZt276+tOIBqFXBNd06a3RxYl9XW4OSCUu5vLMwYQQAyqGUyRPP8L5uUeGlTQ7JnR0LACcY3ASLE/sSCUsdLfUaGp9VP8EOQBmVsvPEmyS9KbSSANhUgpMIar3FTnK7m4fGZ3V8lGHIAMonnnPuAdS84Ql3rFkiYam1uS7i0pSfv+RJ//CMHCe01aAAYAmCHYBI+F2xbc11SljWGkdXP3/m71wmp/HpTMSlAVCrCHYAIuHvwtC1CbphpaUzf1nyBEC5EOwAVJzjODo25AW7Gp844etsCQQ7JlAAKBOCHYCKG52c11wmJ0nqbt8cwa4jsAjzAC12AMqEYAeg4gYCe0t21fiuE750KqE2b5II24oBKBeCHYCKOzYcCHabpMVOWmydHKArFkCZEOwAVFz/sBtskglL7c2bY/KEJHV74wn7R1jLDkB5EOwAVJzfYtfT0ahEovaXOvH5LXZjk/Oam89FXBoAtYhgB6Di/Bar3o7GiEtSWcGJIsfH6I4FED6CHYCKyuedhTFmfZ2bONgxzg5AGRDsAFTU4Piscnl3S62+zqaIS1NZ3e2LQZYlTwCUA8EOQEUNBGbE9m6yFrvmhpTq00lJ7D4BoDwIdgAqKrjUyWbrirUsS53eFmq02AEoB4IdgIrylzqpSy8u2LuZ+FuoMcYOQDkQ7ABU1DFvRmxfR5Msa/MsdeLb0u2OKxwcm1XeG2sIAGEh2AGoqP7AGnabkd/9nMs7Gp5gazEA4SLYAaiYTDavoTE3zGy28XU+ljwBUE4EOwAVMzA6I7/zcbPNiPUFF2XuJ9gBCBnBDkDFDI4tdj32dW2uNex8na0NSifdj94nhqYiLg2AWkOwA1Ax/lZiktS3ScfYJRKWujvc7tijgwQ7AOEi2AGomGNDbrBrakipuTEdcWmi44faJ4am1zgSADaGYAegYvxg17tJW+t8vd5WaiMTc5qezUZcGgC1hGAHoGKODrtdj5t1qRPfrq2tC7ePjzGBAkB4CHYAKmJmLquxyXlJtNht72leuE13LIAwEewAVMRAYGmPzR7sejoalUi4u24cOT4ZcWkA1BKCHYCK8LthJamno2GVI2tfMmGp29sz9shxZsYCCA/BDkBFHBtebLHbrLtOBPnjDJ8YpMUOQHgIdgAqwm+Z6mqrVzqVjLg00evxthY7PjqruflcxKUBUCsIdgAq4rA3lqy3Y3PuOLFccJzhsWEmUAAIB8EOQNnNZXIaGGYNu6Dgki9PsAMFgJAQ7ACU3dGhKTne7V7G10mSutoaZLkTY9kzFkBoCHYAyi4483Oz7hG7XCqZULc3zu4oa9kBCAnBDkDZHfG6GhMJS11tm3upk6CtXe54Q7piAYSFYAeg7PwWu77ORiWTfOz4tnjBrn9kWplsPuLSAKgFfMICKDu/RWpbYCstLAY7x5H6mRkLIAQEOwBlNTOX1dD4rCRpezfBLqivc3HpFyZQAAgDwQ5AWR0JjB/bSrBboq+zUd7EWMbZAQgFwQ5AWQU3ud/Ww+LEQXXppDpa6yUR7ACEg2AHoKz8iRPpZEI97Sx1spw/7vAoY+wAhIBgB6Cs/K7YLd1NSiSsNY7efLZ1u62Yx4amlc0xMxZAaQh2AMrKD3Zbu+mGLcRvscvlHbpjAZSMYAegbMan5zU+NS9J2sbEiYJ29rUu3H6sf3KVIwFgbQQ7AGUzMDKzcJs17Arb0tWkVNLton6sfyLi0gCodgQ7AGUTXOpkO8GuoGTCWlio+BDBDkCJCHYAyubwgNu1WJ9OqtNb1gMn8rupHxuYVN5xIi4NgGpGsANQNo97wW5rd5MsixmxK/G7qefmc0u6rwFgowh2AMrCcRwd9hYn3trFjNjVnLazY+H2EWbGAigBwQ5AWQyOzWp6NitJ2sr4ulXt7GuRv8TfoWOMswNQPIIdgLJ49Inxhdu7trREWJL4S6cS6vZ25ThIsANQAoIdgLI46M3wTCQsbe8h2K3FX8D50LFxOUygAFAkgh2Asnj0yJgkd3xdOsVHzVq2drnd1RPTGY1MzEVcGgDVik9bAKHL5vI6eNRtsWP9uvUJbrnGenYAikWwAxC6w8cnlfE2tN/RSzfsemwJzBxmazEAxUqV8mRjzAclPVXS+ZJOlZS3bbukcwKofsGJE7TYrU9DXUrdbQ0aGp9dWP8PADaq1Ba7v5T0YkmPS+ovvTgAaoEf7JrqU+pqa4i4NNVjR5/busmSJwCKVWqwO8227U7bti+RZIdRIADVzw92u7a2suPEBuzodVs3h8ZnNTE9H3FpAFSjkoKdbduPhFUQALVhajajY8PTkqTd29oiLk11CY5HZJwdgGIweQJAqA4cXRxft2dra4QlqT5+i53EzFgAxamKiQ6dnewzGZRMunmcelmKeims0vVy9M7DC7dP3t6muUxe6XRSCW/PrHQ6UdL3YZzDsiRLilWZ0umEOlrr1dZcp/GpeR0dnq74tczvUGHUS2HUSzzRYgcgVA8+PipJ6uloUEtTXcSlqT57trmtnPsPjbADBYANq4oWu5GR6aiLECv+X0fUy1LUS2GVrBfHcWQfGpEk7d7SqnzeUSaTVyaTk5VOSlLJ34dxDseRHEn5vBObMvnfn7qjQ/c8PKShsVk9eHBYfR2Npf5Y1o3focKol8KolxP19kY//IQWOwChGZ2a1+RMRhITJ4p16s72hdsPPjYaYUkAVCOCHYDQPBJYmHgPwa4oO3pbVO+14tmPj0RcGgDVhmAHIDSPHBmTJCUTFluJFSmZsLTLm03sj1cEgPUqdUuxKyXt9r7dLckyxnzIf9y27Y+Ucn4A1cXvOtze26x0ir8bi7V7a6seenxUx0dnNTw+y+4dANat1MkTV0l63rL7/iJwm2AHbBKTMxkd9NawO31nR8SlqW5PPrlbN93xuCS3e5tgB2C9Sgp2tm0/P6RyAKhy+w+NyF+c4/STCHal2L2tVelkQplcXvbjo3r6mX1RFwlAlaCvBEAo9nvjwZIJa2EtNhQnlUxoR587RnH/ISZQAFg/gh2AUNx3YFiSdNKWVqVTyTWOxlp2bXHD8RODUxqfmo+4NACqBcEOQMkGx2bUP+wuUnoyy5yEwg92ErNjAawfwQ5AyR44uNhdePJ2gl0YdvS2KOntI/uQt4wMAKyFYAegZPd748Aa61Pa2tUccWlqQzqVWGi1s9mBAsA6EewAlCTvOLr/oDu+7vSTOpTwWplQulN2uNuLPd4/oanZTMSlAVANCHYASnLk+JQmpt3QYXZ3Rlya2nLKDrdb2xHj7ACsD8EOQEke8FrrJMnsYv26MO3Z2rYwzm7fo8NrHA0ABDsAJfLH13W01qu3ozHi0tSW+rrkwmLPdz88KMdx1ngGgM2OYAegaNlcfmFg/xkndciyGF8XtnNO7ZYkjUzM6VD/RMSlARB3BDsARXtsYFJzmZwkxteVy9lesJOkXz40GGFJAFQDgh2Aot314PGF22YXwa4cOlsbtK27SRLBDsDaCHYAiuI4jm6/v1+StGdbm9qa6yIuUe0697QeSW4L6dg024sBWBnBDkBRDhyd0ODYrCTpKaf3RFya2uYHO4lWOwCrI9gBKMpdDy12w54bGAeG8O3sa1FrU1qSdJd9fI2jAWxmBDsAGxbshj2pr0XtLfURl6i2WZalM05yxzDef3BYM3PZiEsEIK4IdgA2LNgN+6Q9XRGXZnM4w1vPLpd3tJ+9YwGsgGAHYMPu2O+21lmSztxNsKuE3VvbVJd2P7J/8RDdsQAKI9gB2BDHcbTXG+d18g5mw1ZKKpVYWFLmlw8NKpvLR1wiAHFEsAOwIY8eHdeQ1w371DP6Ii7N5nL2Ke4klcmZjPY9MhRxaQDEEcEOwIbs3T8gye2GPY9lTirq7FO7VV+XlCT9ZN/RiEsDII4IdgDWLZlKLHTDnrKjndmwFVafTuqCM91W0rsfHtTIxFzEJQIQNwQ7AOt2/4GRQDdsb8Sl2ZwuOnubJMlxpJ/SagdgGYIdgHW7ae9jkqR0KqELnsT4uiictKVFW7vcvWN/cvcTyjtOxCUCECcEOwDrMjQ2q7sedLthn3pGr5oa0hGXaHOyLEvPOtdttRscm9VDh8ciLhGAOCHYAViXn9xzVH7j0LO9YIFoPOOsLUolLUnSj375RMSlARAnBDsAa8pk8/rhLw5Lknb2tmhHb0vEJdrcmhrSCwtD793fr4np+YhLBCAuCHYA1rTXHtD4dEaS9LQzGVsXB/7klWzO0e3eEjQAQLADsKYf/OKIJKmlMc3esDGxa0uretobJEn/fcfj7EQBQBLBDsAaDh2b0MPeAP2LztmqVJKPjTiwLEsXP2W7JGlgZEa33dcfcYkAxAGf0ABWdfOd7tg6y5Kefe72iEuDoAufvFXt3l69N956ULk8rXbAZkewA7Ci/pEZ/exedxHcs0/pVldbQ8QlQlA6ldCLnn6SJFrtALgIdgBW9KWbH5LjuK11r3j2yVEXBwVcdM42tTS6awreeAutdsBmR7ADUJD9+KjufnhQknThWVu1rac54hKhkLp0cqHVrn9kRnfagxGXCECUCHYATpDPO/qPmx6S5Hb3vfjCXRGXCKu5+CnbF1rtvvnTR5XPs80YsFkR7ACc4Pb9A3qsf0KS9KxztqnNG6CPeKpLJ/XMs7dKko4OTeu2BxhrB2xWBDsAS8xlcvrKDx6WJLU31+nCs7ZGXCKsx9NM30IA/9JND2l8it0ogM2IYAdgiRtvOaiRiTlJ0suffbLq0smIS4T1SKeS+pXnniJJmpzJ6D//5+GISwQgCgQ7AAsOHZvQf932mCRpZ1+LnnHWlohLhI0457QenXtajyTp1vuO6Z5HmEgBbDYEOwCSpGwur+u+84DyjqNEwtLrX2KUSFhRFwsb9LpLTlNDndvK+q/fe1Azc9mISwSgkgh2ACRJ37/jcT02MClJetHTT9KO3paIS4RitLfU63KvS3ZofFbf+PGjEZcIQCUR7ADoyOCUrv+JGwC2djXpJRfujrhEKMVF52zTKTvaJEk33XlY+x4dirhEACqFYAdscplsXl/4r/3K5ty1z173wtOUTvHRUM0SlqXffLFRXdr9OX76+nt15PhkxKUCUAl8egObWD7v6PPfvl+PHBmTJF141hbt3toWcakQhr7OJl3+nFMlSbPzOV379X2anMlEXCoA5UawAzYpx3H0xe/buuOBAUnSKdvb9PzzT4q4VAiT2dWplz7T3TVkYGRGn/nWfewlC9Q4gh2wSd1wy0H98JdPSJK2dTfrd159Nl2wNeiFF5yk887olSTdd2BY//q9B5V32HIMqFV8igOb0Pf3Pq7rf3JAktTVVq+rf/UcNTWkIy4VysGy3KVrdvQ2S5J+fPcTuu47+2m5A2oUwQ7YRPKOoy//z8P60k0PSZKaGlJ6y+Vnq72lPuKSoZzq00n93q+co96ORknSz/Yd1We+eb8yWcIdUGsIdsAmkc3l9fkb7td3b3d3lmhrrtPrX3zmwn/2qG3tLfW68qVnLrTc7bUH9Hdf38cCxkCNIdgBm8DY5Jyu+eo9uu3+fklSX2ej3v3rT9WWrqaIS4ZKampI6/d+5ZyFNe72PTqkP//CHXqcpVCAmkGwA2pY3nF00+2P6e0f/6HuOzAsSdqzrU3v/vWnqru9IeLSIQqN9Sld/Zpz9eSTuyS5s2U/fN0d+v4djyufZ1IFUO1SURcAQHkMjM7ouu88IPux0YX7ztrTpStfZtTcyESJzawundRbXn22bvjJAf3grsPK5R196eaHdO+BYb3x5U9Sd3Nd1EUEUCSCHVBj+oendeOth3TLvUflr2rR0VKn115yurZ2NSudSkZbQMRCwrJ00dnb9ORTuvTv37M1ODarex8d0vuu/akuMH161cV7tJP9goGqQ7ADasSR45P67s8f06339S+sU2ZZ0nPP26FXPudk1aWSGhieibiUiJtdW1r1/iufpht/ekA/u+eocnlHe+0B3fnggC4wfXrx00/SqTvaoy4mgHUqOdgZY14j6f2SzpE0L+mnkv7Ytu17Sj03gNXNzGX18wf69dN7jurRJ8aXPHbeGb16xpO26MmndiuZsBg/hRU11KX02ktO1/ln9unWfcd0+/3H5DjSHfsHdMf+AZ2+s12XXnCSzjmlW/V1tPgCcVZSsDPGXCXp85LulfQBSfWSfl/Sz4wxF9u2fXfpRQQQNDIxp7sfGdS+R4Z034FhzS9bi+wpp/XoZRft1vbeFlrosCGdrQ36zZcYvejpJ+m/b39Md+4fUC7v6KHDY3ro8JiSSUun7+zQ2Sd3yezq0Mlb25RIWFEXG0BA0cHOGNMp6ROSDkt6tm3b4979/ynpfknXSnpuGIUENqu5+ZyODE7pwNFxHeqf0IGj4zpyfOqE49pb6vTMJ2/VaTs7dPL2NtWlaVVB8bZ0NemKl56pi87epn2PDOrWe49pZi6rXM7R/kMj2n9oRJK7wPWTdnXqrJO7tKuvRVu6mtTCxBwgUqW02F0uqU3SJ/xQJ0m2bR82xnxZ0lXGmD22bR8ssYxATXIcRzNzOY1Ozmlkck6jE3Pu7Yk5HR+d1dGhKQ2Oza74/NamtE7Z0a4LzuzTOaf2KJGwaKFDqFqb6nTZs/boZc/aowcfG9EvHxzUoWPjGhhxr7Pp2azufPC47nzw+MJzWhrT2trdpC2dTeptb1BfV6N62hrV2pRWS1NaTfUpWRatfEC5lBLsLvS+3lLgsVskXSXpGZIOlvAakZqdzy7ZcmfJCKVlm2g7Kz+09Lg1Nt8OPuxo2bHet1nHvTk2PrP8oYInOvGxgjdPeP0TC7fKt6vUx2rP20h9nFgcZ8lxw9PzkqSJ8bkCxxZ+3vIHV68rR/m8o1zeUTbnKJ/PK5dzlPW+5vKOcvm895ij+UxOs/N5zWaympvPaXY+p7lsTvPzOU3PZzU6Pq+5bG7V9x8cz9RUl1JfV6NO2d6us0/t1s4tLRoamVN7a1rJpPsfZcKyZFnupInl31vW6o8X+n49x1T6+3DO6dVJSOeLQ72EU6bC9VKfTurc03q0ratF7a1pTc9m9eDjo7rv0SEdOjqhydnMwmtmcnk9PjCpxwcKL3qctCy1NKXV2pRWc6Mb9OpSSaVTCaWTCaXTCdWlEkqnEkqlEqpLJhYeT6YSSiYsJSxLCUtKJCz3nxX4l3AfswJdxMH35N+w/AcW71pyjOV9Z1lS3jtubGJ2SSi1rGXPK6cKheGNvIrl9QxMeJ+9K0kkLDWzF3XFWGv9x7oSY8wNkl4h6Szbth9Y9tiLJX1P0ntt2/5EkWXrlTRQ5HMBAACi0ifp+JpHlUEpO0/4exGd2DwizS47BgAAAGVWSlfstPe1vsBjDcuOKcag3MRb6nkAAAAqwW/QGoyqAKUEu8Pe152SHlj22M5lxxTDUUTNmAAAAEU4cdmCCiulK/Z27+tFBR57lvf1jhLODwAAgA0oJdhdL2lC0luMMW3+ncaYnZJ+TdJPbds+UGL5AAAAsE5Fz4qVJGPM70j6jNydJz4jqU7S2+XOaH2Obdu/CKOQAAAAWFtJwU6SjDGvlfQ+nbhXLNuJAQAAVFDJwQ4AAADxUMoYOwAAAMQIwQ4AAKBGEOwAAABqBMEOAACgRhDsAAAAagTBDgAAoEYQ7AAAAGoEwQ4AAKBGEOwAAABqRCqKFzXGXCrpNZKeKulcSY2SrrRt+4tFnOs1kt6vE7c0u6fAsSlJ75X025L2SBqS9E1JH7Jte6ioNxMyY8xuSX8p6VJJLZIelPR3tm1/bp3P/zNJf7rGYTtt2z7iHf98ST9Y4bg7bdu+YD2vW06l1ol3jtW2WGm1bXty2fFNkv5E0q9L2ibpqKQvSfqwbdvTG3sH5RHCtXK6pNd7zz9VUqukQ5JukvSXtm0fXXb88xWTa2Ujv/crPH9DP98wrsFKKKVejDGvlHS5pIsk7ZI0K+lhSZ+T9C+2bWeXHf8FSW9c4XRvt23774p8G6ErsV7eJOm6FR7+mm3bry3wnHMl/R9JF8vdQ32fpL+2bfvrRb2BMiixTg5K2r3KITfZtn1p4PgvqAquFWPMB+XmkvPlfibmbdvecE6K+vMlkmAn9z+T10u6X+4F/4xiTmKMuUrS5yXdK+kDkuol/b6knxljLi6wX+11kq6QdKOk/yc33L1L0nONMc+0bXuimHKExRizU9JtktolfVLSAUmvlPRZY8wu27b/9zpO83W5H8bL7Zb0EUl3+aFumc9K+smy+yIPuyHVie8nct/ncrPLXjMp6TuSnifpXyX9WO6H3x9IutAY8yLbtnMbfS9hCqlerpL0DknflvQVSdOSninpakmvN8Y827bt/QWeF+m1UsTv/fLnb+jnG/I1WDal1ovcADcj94/d+yS1yf2P6R8lvcYY80rbtgv9gXRlgftuL+5dhC+EevF9VNIDy+47VOD1niI3JM1J+rik43L/3/maMebNtm3/Y1FvJEQh1Mm75AaQ5a6Q9BJJ31rhebG+VuSGq1FJv5D7/no3eoI4fL5EFez+WNLv2bY96/01tOFgZ4zplPQJSYclPdu27XHv/v+UGxivlfTcwPGXyL3ovmXb9uWB+2+XdL2k98lN2FH6qKStkn418Jfd54wxX5f0h8aYf7Ft+6HVTuD9tVWotfIvvJuFgo0k3VpMi2kFlFwnAY+u8z2+Ue4v5bW2bb/Dv9MY86jcX7w3Svqndb+D8gijXr4q6a9s2x4J3PdZY8xtkj4j6cOSfq3A8yK7Vjb6e7+Cjf58w7wGyyKkerlC0s3B8GaM+aSkH0p6uaSXyf0Pa4mYfm5ICq1efP9t2/YP13HctZKaJb3Atu293uv9o6RbJH3CGPM127ZHN/RGQhRGndi2fX2B8ybkNh7Myg00hZ4X22vFc5pt249IkjHmhyoi2CkGny+RjLGzbfuIbduzax+5qsvl/kX5ef/C9M59WNKXJT3HGLMncPwbvK+fWFaWb8pt4XqDIuQ13b5W0oECzfWfkJSU28pZzLmTkn5L0pSk/1itDMaYhmJeoxzKUSfGmDpjTOsah/nXwseX3f8ZuXVYE9eKbdt7l4U635e8r+euVoaIrpWN/t4Xsu6fbzl/L0NWcr3Ytn3T8hY5r2XhK963Ba8HY4xljGnzPmfiJozrZYExpsUYU7fK43skPUfSj/xQ571eVtLfemV59QbfQ9hCrZOAF8vtGfrKSsE15teK/FBXosg/X6p58sSF3tdbCjzm3xdsCbxQUl5uk+dyt0rabYzpC694G3aO3LGGtxZ47OeSciqyy1ruX9o7JP1n8Bd5mWvkXnQzxpgDxpgPGWPSRb5eWMKuk9fK7W4cN8YMGWM+b4zZEjzAGGNJukDSE7ZtL+lm8f4YuUvSBd5xUSnntSK514okDazweJTXykZ/75co4udb7roOS0n1soa1rodRSWOSZo0xPzLGvLDI1ymHMOvlm5ImJM0ZY+43xry1wOdAOX8OYSlXGd/sfV1tXFicr5WSxeXzpZqD3U7v6+ECjx1edox/e9C27bl1Hl9pK74f27Yzcj9Uiy3fW7yvhbphM3LHHH5Q0qsk/a6kxyX9haTrveb1qIRZJ3fI7Vp8rdxxHjfKnUTz82XhrktuN0qh68ovS7OkznW+bjmU81qR3O4U6cTu5jhcKxv9vV9uoz/fctd1WEqtl4K88T+/K2lE7pCVoH65rVBvk9sK9WeSzpL038aY39zoa5VJGPUyLbcV+71yxz79vqSspL+X9OkyvF65hV5Gr1HkVZL227a9fPytVB3XShhi8flS9Bg7Y0yL3A/49fqabdu/KPb1CmjyvhYKarPLjvFvF+p2Wun4opRQL6u9H8kt44bLZ4zZJukySfts2/758sdt2/6Z3A+r4HM+J/eD7NfkBqEvb/R1l50v8jqxbXv5Xz1fNMb8XNKn5M4ivtq7fz2v6R83vJ7XXkkc6qVAmf5I7oz16yX9c/CxSlwr67DR3/uNPH/5OYbXeXzJnxshKLVeTuBdn9+U2233q7ZtL7nebdv+wLKnfNO4sx/3SbrWGPMN27ZnNvKaZVByvdi2/WUtu66NMZ+R9CNJv2uMuS7w2bri69m2PW/c2flRXy+hXyuS3iQprRVa66rkWglDLD5fSpk80SJ3EsR6PSx3pklY/CnD9QUea1h2jH+70LErHV+sYutltfcjuWUcLKI8vyX357zuadO2bQ9aoA4AAAYiSURBVDvGGH/g/MtV+n/WcasT36fl/uX48sB963nN4HGliFW9GGPeKXeJhh9Kev0KMyCXKMO1spaN/t5v5PmFzlHuazAspdbLEl6o+47cpR9+37btb6znebZtH/EmCvyB3BnWKy2PUymh1ovPtu2sMeajcluwL5Pbbbbq63lj86xiXi9k5aiTq+QumfIv631CDK+VMMTi86XoYGfb9jG5F2lUgk3Gy6egF2rePCzpDGNMfYHu2NWapjekhHpZsQncG7/UJ+mXGzmh149/lVaZpbSKg97XYmYFLRGnOllWLscYc0jSkwN3D8v9ZVup+Xun3PFlK7X+buT1Y1Mvxpj3yB3se7OkVxVaa2kVB72vJV8r67DR3/vlNvrzLes1GKJS62WBN7novyQ9S9LVtm3/wwbLctD7WonrYS2h1UsBB72vwfe5WldmaP/PlCjUOjHGPE/SGZK+ZNv2RkPIQe9rHK6VMMTi86Wax9j5a99cVOCxZ3lf71h2fEKLA0eDLpJ0yLbtlQYHV8I+uWtIFXo/F8qdHbPR9X5eKOkUrTJLaRWne1+PbfB5YSpHnSzwxoSdosB79Fqp9krabtxFI4PH18tduHLvelqzyijUejHGvF9uqPuupFdsMNRJlb1WNvp7v0QRP9+yXoMhKqlefMaYdknf987z5iJCnRSPzw5fKPWygkLvcz2vF/X1Enad+GO4i1lMN07XSsni8vlSFcHOGHOqMebMZXdfL3eG0luMMW2BY3fK7Rb6qW3bBwLH+y1W71127ldJOk0bb9EKlfef6dclnWzcFcGD3iN3dsySpUpWqJegNWcpGWO6C9yXktslJ6280GTZhVUnhd6j54NyB7suf48FrxW5g8ibVUPXijHmDyX9ldwupVevtgxRTK6Vdf/ee0uynOmNMw1a98+3mLqOSMn1Egh1T5f0Jtu2V1yr0RjTXGi5G2PMGXJ7CQa02D0ZpTDqpdB136TFHX5u8O/3zvUzSc83xjwtcHxK7mLgE3LHLUYpjN8h/zmdkn5V7jCRgl2pVXStbEicP1+i2lLsXLkzaCR3DIckXW4W1875lr10W5Ob5a6Ps9BtZdv2iDHmD+SuDfMzbzBrnaS3yw2s7wg8X7Zt32SM+Q9Jv2GMuUHuL9ceSe+WtF/Sx0J7g8X7I0kvkvSv3ofCAbn19Eq5WzzZy44/oV58xpgeSb+ilWcp+b5rjDkq6U5JR+QulPjrcrsnv6zoP4TCqJMPGWMukvvBc0juYNRLJb1U7s/+w8vOcZ3ctYbe7v1n92O5a3i9Te6OC18I682VoOR6McZcLXdxzH65Hy6vM8YEnzO5bCHSyK+VDf7eP0Puz/yf5Q7u9m3057vRuq64kOrlJrlLNXxTkmOMuWLZy9wT+Fw+XdJ/GWO+KekhSZNyZzleJXe80BUrrEBQUSHVyz5jzE/kLvzeL3e7tTd6Xz9WYFLgO+ReU98zxvyN3DFSr5cbmH93hbUjKyakOvFdIXcc2OdX6cWoimtFkowxV2pxq7TdkixjzIf8x23b/kjg8Nh+vkS188T5cpdICHqt909y+53X3K/Otu3PGmOG5e4a8ddaut9doS1R3ii36fO35M6GHJb0Rbl7xa60vlvF2Lb9mBdAPio33ft7xv2eVt4xYiVvkPvLulbz+FflXkRvkzsFe1puHb1F0j9G3OUYVp38QNKT5C5z0iN3PcNH5C7r8df2sq3kbNvOGWMuk7sTyf+S9Bty9/r7hNy9/iLdTkwKrV78mcJbVHgnjUNausRFLK6VIn7vlz9/Qz/fkH8vy6bUepEb6iR3AdvLCzz+51r8XD4mNwg+T279NclteblBbti5q9j3EbYQ6uU/5L7PF8rd9mlC7h8377Ft+2sFXu8uY8yz5bZkv0+Le8W+zrbtr4bwlkoWQp343ix36ZcvrHJM1VwrcsPm85bdF8wqH9Ea4vD5YjlOpP9vAwAAICRVMcYOAAAAayPYAQAA1AiCHQAAQI0g2AEAANQIgh0AAECNINgBAADUCIIdAABAjSDYAQAA1AiCHQAAQI0g2AEAANQIgh0AAECNINgBAADUCIIdAABAjSDYAQAA1AiCHQAAQI34/yMRVgWyF2eJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# last_state_dict = copy.deepcopy(net.model.state_dict())\n",
    "\n",
    "# Nsamples = 10\n",
    "\n",
    "# for idx, iteration in enumerate(savemodel_its):\n",
    "    \n",
    "#     net.model.load_state_dict(save_dicts[idx])\n",
    "#     weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "#     fig = plt.figure(dpi=120)\n",
    "#     ax = fig.add_subplot(111)\n",
    "#     symlim = 1\n",
    "#     lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "#     sns.distplot(weight_vector, 350, norm_hist=False, label='it %d' % (iteration), ax=ax)\n",
    "#     ax.legend()\n",
    "\n",
    "#     ax.set_xlim((-symlim, symlim))\n",
    "\n",
    "\n",
    "# net.model.load_state_dict(last_state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print('a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SNR (dB) density: Total parameters: 43070400')"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "\n",
    "# mkdir('snr_weights')\n",
    "SNR_vector = net.get_weight_SNR()\n",
    "np.save(results_dir+'/snr_vector_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(SNR_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(10*np.log10(SNR_vector), norm_hist=False, label=name, ax=ax)\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('SNR (dB) density: Total parameters: %d' % (len(weight_vector)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SNR (dB) CDF: Total parameters: 43070400')"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "\n",
    "# mkdir('snr_weights')\n",
    "# SNR_vector = net.get_weight_SNR()\n",
    "# np.save('weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(SNR_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(10*np.log10(SNR_vector), norm_hist=False, label=name, ax=ax, hist_kws=dict(cumulative=True), kde_kws=dict(cumulative=False))\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Cumulative Density')\n",
    "ax.legend()\n",
    "plt.title('SNR (dB) CDF: Total parameters: %d' % (len(weight_vector)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate pruning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# net.load(models_dir+'/theta_last.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]\n",
      "-inf\n",
      "params: 2395210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete proportion: 0.000000\n",
      "\u001b[34m    Jtest = 0.109391, err = 0.026600\n",
      "\u001b[0m\n",
      "-23.970589 dB\n",
      "params: 2155689\n",
      "Delete proportion: 0.100000\n",
      "\u001b[34m    Jtest = 0.108832, err = 0.027400\n",
      "\u001b[0m\n",
      "-21.093139 dB\n",
      "params: 1916168\n",
      "Delete proportion: 0.200000\n",
      "\u001b[34m    Jtest = 0.106589, err = 0.026000\n",
      "\u001b[0m\n",
      "-18.124451 dB\n",
      "params: 1437126\n",
      "Delete proportion: 0.400000\n",
      "\u001b[34m    Jtest = 0.104356, err = 0.026600\n",
      "\u001b[0m\n",
      "-15.006608 dB\n",
      "params: 958084\n",
      "Delete proportion: 0.600000\n",
      "\u001b[34m    Jtest = 0.100686, err = 0.025800\n",
      "\u001b[0m\n",
      "-10.770768 dB\n",
      "params: 479042\n",
      "Delete proportion: 0.800000\n",
      "\u001b[34m    Jtest = 0.095435, err = 0.026300\n",
      "\u001b[0m\n",
      "-9.388739 dB\n",
      "params: 239521\n",
      "Delete proportion: 0.900000\n",
      "\u001b[34m    Jtest = 0.094517, err = 0.026600\n",
      "\u001b[0m\n",
      "-5.429064 dB\n",
      "params: 23953\n",
      "Delete proportion: 0.990000\n",
      "\u001b[34m    Jtest = 0.095008, err = 0.027500\n",
      "\u001b[0m\n",
      "-2.286946 dB\n",
      "params: 11977\n",
      "Delete proportion: 0.995000\n",
      "\u001b[34m    Jtest = 0.103515, err = 0.030300\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delete_proportions = [0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]# 0.9999, 0.99999, 0.999999, 0.9999999, 0.99999999] # \n",
    "# keep_proportions = 1 - delete_proportions\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "Nsamples_predict = 100\n",
    "# Nsamples_KLD = 20\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "err_vec = np.zeros(len(delete_proportions))\n",
    "\n",
    "print(delete_proportions)\n",
    "\n",
    "for idx, p in enumerate(delete_proportions):\n",
    "    if p > 0:\n",
    "        min_snr = np.percentile(SNR_vector, p*100)\n",
    "        print('%f dB' % (10*np.log10(min_snr)))\n",
    "        og_state_dict, n_unmask = net.mask_model(Nsamples=0, thresh=min_snr)\n",
    "#         print(net.model.state_dict()) # for debug purposes\n",
    "        print('params: %d' % (n_unmask))\n",
    "    else:\n",
    "        print('-inf')\n",
    "        print('params: %d' % (net.get_nb_parameters()/2))\n",
    "    \n",
    "    test_cost = 0  # Note that these are per sample\n",
    "    test_err = 0\n",
    "    nb_samples = 0\n",
    "    for j, (x, y) in enumerate(valloader):\n",
    "        cost, err, probs = net.sample_eval(x, y, Nsamples_predict, logits=False) # , logits=True\n",
    "\n",
    "        test_cost += cost\n",
    "        test_err += err.cpu().numpy()\n",
    "        test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    test_cost /= nb_samples\n",
    "    test_err /= nb_samples\n",
    "\n",
    "    print('Delete proportion: %f' % (p))\n",
    "    cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))\n",
    "    err_vec[idx] = test_err\n",
    "    \n",
    "    if p > 0:\n",
    "        net.model.load_state_dict(og_state_dict)\n",
    "    \n",
    "    \n",
    "plt.figure(dpi=100)\n",
    "plt.plot(delete_proportions, err_vec, 'o-')\n",
    "\n",
    "np.save(results_dir+'/snr_proportions', delete_proportions)\n",
    "np.save(results_dir+'/snr_prune_err', err_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight KLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'KLD density: Total parameters: 23928000, Nsamples: 20')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "Nsamples = 20\n",
    "# mkdir('snr_weights')\n",
    "KLD_vector = net.get_weight_KLD(Nsamples)\n",
    "np.save(results_dir+'/kld_vector_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(KLD_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(KLD_vector, norm_hist=False, label=name, ax=ax)\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('KLD density: Total parameters: %d, Nsamples: %d' % (len(weight_vector), Nsamples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'KLD CDF: Total parameters: 23928000, Nsamples: 20')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+ExbmJzkFeJfwBVptjxPmmNiL0NE1Zy9Cijze52A+xc+SshR0SDSzZQmdB3vT9La1mY2I9SkZROhM959Ys06xY69HmJyr1ImJ+qr8hSYDcwBz9+4C4Vx5Cp/vBwmTMI2Lznh7JfqyftHMDic0gX2znP1EAckThCbIzd29Q0aqG5tFt28VKeN0oiDUzPYkBCj7Fxx/B8Ln6WFg92LfE+7eYmEU2TpmligITHKdjP8XbftfM3sN2MfMrs5tG3USXYr8PhV3Ezpj/wi4KrZ8P8KEa/HP0F3Avma2RO6HMPou2o6Oc7qUzd3vsjDs9JyoDF92st08Qh+MeYT5kNYgdMDsr89Ezhgz27ygCWdPwvfz01087n5CdvQNj0YKdifq+Hxf1Gflt4Rm3x4HJWb2E0JAci2xfm1F9Mtr3h0FJRXi7mdHgck5hMBkj4IvnO3MrMPZpecP0V0h+qFKEtJo6xJSu83APt7NrHru/nn0pXc/8C8Lc408S0hFrkA4q1idjp2/1jKzzwlnYEsS+mf8iPAB2M7LmB/DzJ4A1nP3wvRnue4hDCe93MLMuf8A1iJkqJ4htL/mvAxsaWFyoPeAudFzd3/0+IsJZ6njgDMIX/Bf7UXZPid8Yf6c8FwfSWhui/dTuB84Kjoz/DthiOxphKCvVH1V/jwehqEfDVwTNRPcTTgLXBxYE2iKnW3lztSPMbM/EM1S6u6vR8/HJWa2MqGz3izCWeV6hLb587sqh5ntTvjCv5fwPKUIKfDBxDr/mdkvCKNy1nX3f3axv68R+mgsSugE+bVoWc57uSDFzC4kpO6fITRfjiT0C9gfmBRvrowCkMUIfVOGET4/EwgztT4W224LQkAyhWgyQ2sfRg1hfp5cU88lhM6RfzKzmwk/vFtG+72noFPucYT3xu/M7EZCMPQLwmekrZ+Vuz9vZrcTTlpShB/1bQmB/ZEFzUw/B35AGMVyNqFfzGmEH/q82ZUt9J/ahS5mre7GiYTPNrQ3Z+SGnDcTguQPCd+JxxD61+QmauyXz0TMp8At0XMyhTBUdg/gwi46uUIYTfcs8Ez0vfwmYQTZcoTmzD3cfYaZ/Y4wuuzvhEzYUoSTw6nEhqub2ceEz9AaXRXWwuzjVxLex78B1i14z70Q+50q+TXvSwpKKsjdfxYFJucSmnJ+GFt9cycPi3cmzfXqb6V9mvmb6cE089EXz6qEcfg/IHzgU4T04OMU79CXG88/n3Cm93L0uEne+dwO3Wmggu+vKNW7NaEPz2HR7SeEtvfTCwLAk4ERhKF7wwmZiPGEs8ORhDPtwwlfDKcSzroO7UXxbiV8WV1ACOreBHb1/Cn3TycEfROi/18izEFyIOEMqBR9Vf4O3P16CxOAHUfIwA0lPN//Ir8Z8UHCD+gehLOwJKFN/wV3P9XC3CoTCHUdROgr8xz5kzZ15jXCD9CphB+YedGyPdw93jyZiv666wy+BuEHG4pn/44nBAsQmj0OI3yGRhIyR68SnucbCx6XiZYvTzhzf4VwEvG7gu22JARUKxL6EhVaG3gBwN1/bmbvEjpw/ip63BTCe/vy+IPc/aHoZORMwuvxBSGQPLFIZ+T9CB0rTyAEmW8Dh7j79QX7/F/Uj+FCIFePp4GN3f3dgn0OJwScZY36c/enzCw3aCDuGeAQwmsymtAk9hxhgsrc9Av99pmITCEEPecTTiw+i+531iwDgLu/Z2ZrET77pxC+J2YRnv+HaX/uniQE4nsRAt3PCJm9cwqa3Er9ft2W8LnYgPB8FlqcqKm1h695n0lks/3SoVak5lhsQqaoE6JIXYkyLtOBX7r7WdUuT1+yGrk2z0Cn0TciIlKub1FafzeRkqj5RkREyuLhEgmFw7tFyqbmGxERERkQ1HwjIiIiA4KCEhERERkQFJSIiIjIgKCOrvlmEi6L3dNZHkVEROrdVwnzXXV2+YxuqaNrvrnZbLYxk+m/5ySZTNCfx6sm1bX21Es9QXWtVaprZfefSCTm0YsLnCpTku+jTCY7bvr0siYm7LGGhiQjRgxj1qw5tLYWvbp5zVBda0+91BNU11qlulbWyJHDSKUSvWppUJ8SERERGRAUlIiIiMiAoKBEREREBgQFJSIiIjIgqKOriNSlTCZNOp0ucdsE8+alWLBgPul0bY/UUF1rU2/rmkqlSCZTfVCyfApKRKSuZLNZZs2azty5s4HSv5ynTk2SydT2CI0c1bU29a6uCZqahtHcPJJEIlHRcsUpKBGRujJ37mzmzv2S4cMXY8iQRqC0L9hUKlHzZ9M5qmttKr+uWebPn8eXX85k0KAhDB06vOJly1FQIiJ1I5vN8uWXM2lsHMbw4Yv26LENDcman8siR3WtTb2p66BBQ2htbeHLL2fS1DSsz7Il6ugqInUjk8mQyaRpbBxa7aKILHQaG4eSyaT7tLlLQYmI1I1MJnRs7Y8OeyK1Jve5yX2O+uQYfbZnEZEBqi876onUqv743CgoqZJsNsvVf3qZWx56vdpFERERGRDU0bVKpn0+j7+/9gkvvjmVbddbptrFERG6PhNMJPruTLGcq7XfdNN1TJp0Q9v9wYOHsOSSS7H99juy2257LJTZoP6s04MP3sd5553Vdr+hoYExY77KFltsxY9+tD+DBw+u2LEAPvroQ3bbbXvOOed8NtvsuxXddy1RUFIl6ejy0el0ffT6Fhno0sC8eS2drk8kE2T76LLvjUMaKKeXy5AhQ7jssmsBmD9/Hs8//3cuv/yXpFIpdtll98oWsp/0d50uvfRKGhuH0dKygFdffZkbb7yWuXPnMmHCURU/lnRPQUmVZKIzo3QffcmJSOkSiQTz5rXw2rvTaelkyGQqmeiTz+ughiSrjB3J8MZBPc6YJJNJxo9fte3+Wmutzeuvv8qTT05eaIOS/q7TSiut3DY8fM011+L999/jyScnL3RByfz58xkyZEi1i9FrVQ9KzGw3YC9gLWAk8DZwDXCdu3eaRjCzJ4BNiqxa2d3f6IOiVlQm9uWWKSN1KyKV19KaYUFL8ZEFfRWUVNrQoUOZNWtW2/1rrrmCZ599io8++pBhw4az+uprMnHiMYwePRqAO+64neuuu5J77nmYYcPaJ8V6//332HPPXTj//IvZcMPwVfvMM08xadINvP32Wwwd2sSmm36Hww8/iqamJgBaW1u57rqr+Mtf/sz06dNobm7GbBXOOOMchg8vf8KtwjodcMDejB07jjPOOCdvu+uuu4r777+Hu+56kIaG8n7ehg4dRjrdmrfsttt+y+OPP8oHH7zHoEGDWWWVbzBhwtEss8yyedu98spL3HTTdbz66itks1nGjh3HwQcfxtprr1f0WG++6Rx99ATWW28DTj75DP7zn39xxBGHcuGFl3Lffffw/PPPMmzYMHbZZXf22eeAtsfddNN13H77b7nssmu47LKLefNN58ADD2XPPfdh1qzPufrqy3nqqSeZM2cuyy+/PAcd9BPWWae9DBMmHMzQoUPZbLPvMmnSDUydOpVVVvkGJ5xwCsssM7as561SBkJH12OB+cDxwLbA3cDlwC9KeOzTwPoFf+/2SSkrLP7dllkIvuhEZGBqbW2ltbWV2bO/ZPLkx3juuWfZdNPvtK2fMWM6P/rR/lxwwaUceeSxfPzxR0yYcDCtreGHd8sttyaTyfLnPz+St98HHriXUaNGsd563wZg8uTHOOmkY1h++a9z3nkXcthhR/Dkk5M5//yz2x5zyy2TuPvuO9lrr3355S+v5OijT2D06NG0tCyoaJ22335HnnjiL3zxxRdty9LpNA8//ABbbbVNjwKSdDpDa2src+fO5YUXnueRRx5k0003z9vms88+YZddfsDPf34xJ510GplMhsMOO4BZsz5v2+all/7NxImH0NLSwoknnsa5517ARhttwieffFz0uK+88hITJx7KZpt9l1NP/SmpVHsD3gUXnMdSS32Nc8+9kO99b2uuv/5q7r77j3mPb2lp4eyzT2fLLbfmoosuZ+211yOdTnPssUfw178+wUEH/YRzz/0FI0aM4vjjj+TFF1/Ie7z7G/z2t7/i0EMnctppZzFt2lSOOWYiCxb07LWqtKpnSoDt3P2z2P3JZjYcmGBmp7n7/C4eO9Pd/97H5esT8UAkncmSWgg7pYlIdc2dO5dNN80/C9966+3Ybbcftt0/5ZQz2/5Pp9OMH78aO+20NS+++ALrrLMezc3NbLbZ5jzwwL3suOMubduFH/htaWhoIJvNctVVl7H55ltw0kmnt+1v5MiRnHDC0ey774Est9zyvP76q6yzzrrsvPNubdvEg4lK1WmLLbbiyisv5bHHHmGnnXYF4Pnnn+Wzzz5lm22279Hxttlmi7z766yzPoccMiFv2RFHHNv2fzqdZu2112Xbbb/H5MmPs8MOOwNwzTWXs9RSS3PZZde0BRjx7ETcCy88z8knH8suu+zOoYdO6LD+m9/8FocffiQA6667PtOnT+U3v5nE9tvvTDIZcgmtra0cfPDhbL55e6fZp556ktdff5ULL7yU9dffMHr8Buyzz+7cfPP1rLPOOm3bzpgxnSuvvJ6llw4DLVZYYUX22mtXHnro/rY6VUPVg5KCgCTnX0AjoTnno/4tUf+IN9lkMllSKQUlItIzQ4YM4aqrwmiVBQsW4P4GN910LQ0NDZxwwqkAPPvs0/z61zcxZcrbzJ49u+2xH3zwXtuP5nbb7cSECQfzzjtvs9xyy/Pss08zbdrUth/4Dz54j48//ogjjji2LcMCsMYaa5FIJHB/neWWW54VV1yJW2+9hZtuuo4NNtgQs5XbfkQrWadhw4az+eZb8MAD97YFJQ88cC+rrroaY8eO69HxrrjiGpqahtHamubdd9/hxhuv5ZRTjueiiy5rG+3zyisvc+ON1/Df/3peduSDD94HYN68ebz66isccsjheRmPYp599mkee+xR9t33APbd98dFt9l4480K7m/OI488xKeffsqYMWPalq+//rfztvvPf/7N0KHD2gISCH10Ntvsu9xyy6ToqtihTuPGLd8WkAAsvfQyjBu3PK+++nJ9ByWd2AiYDnzazXabmNlsIAU8B5zu7n/t68JVQjwoSWeyDNIEkyLSQ8lkkpVWWqXt/mqrrUFraytXXXUpu+76Q+bPn8dJJx3DRhttwt5778tii4UrvB5yyH7Mn9+epl9jjW+yzDLL8sAD9zBx4jHcd989rL76mm19JmbOnAnAKaccV7QcuSaKffY5gEQiwcMPP8CkSTew2GIj2Hnn3dh//4NKHs7bXZ2WW255ALbfficOPfQA3nrrTUaPXpynn/4bxx57Ug+evWCFFVZs6+g6fvyqDB8+nNNOO5Fnn32aDTbYkI8//phjjpnASiutzPHHn8zo0YszaNAgjj/+KBYsCIn8L76YRSaTYfToxbs93lNP/ZUhQ4awxRZbdbrNiBEjit6fNm1qW1DS2NjY1pcn54svZjFy5MgO+xs1alTURDWHxsZhRY+RWzZt2rRu69CXBlxQYmbfAvYHznL3ruayfRL4DfAmsCRwHPCYmW3i7s/2pgwNDX3f1SYZ/4Am+ueY1ZRKJfNua1m91HVhrGcmU15GMhG7Heg9wHKZgilT3uatt95k+PDhnH32+W0Zi48/Lp583nbbHbn11t+wxx578/TTf+PEE09rW9fcHH60jz76BL7xjfEdHpv7MR48eDA//vEh/PjHh/C//33AAw/cy803X8+SSy7FVlttU5E65YKS8eNXY9y45XjggXsZM2YMDQ2D2HzzLbraTacSCcidJ44duxwA77zzFhtssCHPPfcMc+fO4dxzL2SRRRYBQtNJPGMyfPgiJJNJpk4tlvjPN3Hi0dx7710ceeRPuOqq61liia902GbGjBlF748aNTpW5o7v5ebmZqZPn95h+bRp02hoaKCpqf2aT4XHyC0zW6nbOqRSiT77zRpQQYmZjQHuBJ6nm46u7n5m/L6Z3Q+8CpwObF1uGZLJBCNGDCv34SUbNmNe2/9Dhw5hxKJNXWxdO5qb66OeUD91XZjqOW9eiqlTkx2+VBOJMA9JKvrrTLKLdeVKJRMkkgkaGhJks6XvP1eWwh+H9957B4CRI0fQ0jKfhoZBDBqUavsRe+yxh8NxC57WT6DAAAAgAElEQVSD7bbbnhtuuJqzzjqNIUMa2WKLLdrWL7/8ciyxxFf4+OMP2X33H1KKsWOX5fDDJ3LPPX/igw/eLelHrJQ6xdftuOPOTJp0IyNGjOS73/0ezc2lj/CJv5bxwLr9WCOjq+ouIJFI0Ng4uO3Yjz/+OOl0mmQyPIeLLDKM8eNX45FHHmTvvfcp2oSTO8awYUO59NIrmDjxMI488jCuvfbGtmAjt81TTz3Bd77T3hfnb3+bzOKLL86SS44hmUx2+jytscY3ufXWW/jHP55ta9rJZDI88cTjrLrq6m3lSiQSTJnyNh9++D+WWSY04bz//vtMmfI2u+66W6evVSaTIJlMsuiiQ2lsbCzpee6pAROUmNmiwEPAHGB7d+98FqMi3H22mT0A7NqbcmQyWWbNmtObXZTk88/bjzFr1jySfXjVxYEglUrS3NzErFlza37CuHqp68JYzwUL5pPJZEins3mXcE8kwsRo6eivUILwI5bJZCueKUlnsmQzWVpbsz2apySTyZLJZPj3v/8DQGtrC+6vM2nSjYwduxyrrrom8+bN5/bbb+XCC89n440345VXXuKRRx4Mxy14DhZZZFE23HATJk9+jB133Dm6VH37+gkTjuass05lzpw5rL/+hjQ1NfHxxx/x7LNPcfDBh7PMMsty8snHYrYyK6xgNDU18fTTf2XWrM9ZY41v5e2rN3WK72eLLb7PVVddwcyZ73DiiaeVdIz4sQDeeOP1tj4l7703hZtuuo6RI0ex4Yab0NqaYY01vgXA2WefyQ477My7777Dbbf9luHDFyGTaX8ODzlkAkceeSgTJhzKTjvtxiKLLMJ///sGiy66GNtuu0PbZySdzjBkyFAuuugKjjgibH/FFdez2GKLtW3zwgv/4LLLLmHttdflH/94jocffpBjjjmRTCZ3pets9Pzk13fddTdg5ZW/wVlnnc7BBx/O4osvwT333Mn777/HMcec0Hb8bDbLiBEjOf74ozjwwEPJZuHGG69h9OjF+d73tun0eUynw+vz+edzmDu3Y0NGc3NTrzOnAyIoMbNG4F7gK8D67l5uo1ZFTmN68sauxDFaWtL9csyBIAy/U11rycJUz3S66x/9QV2czffl5Gnlmj9/Poceuj8AqVSKJZYYw/e+tzUHHHAQDQ0NrL/+hhx22ETuvPMPPPjgfay66upccMGl7LFH8Y6MG2+8KZMnP8Z22+1IYXy0+ebfZZFFhvPrX9/Mo48+BMCYMV9l3XU3YOTIUQCsuurq/OUvj3H77b8lnU6z9NLLcuaZP2PttdetWJ3impsXZY011uTTTz9h/PjVSj5G3FFHhdEvyWSS0aMXZ6211ubAAw9ta7Jafvmvc/LJZzBp0g2ccMLRrLDCivzsZ7/g9NPz+6+svvoaXHHFddxwwzWcd95PSSZTjBu3HAcddFjR4zY3N3PJJVcxceLBHH30T9pmsQU4/vhTuOeeO7nrrjsYOnQYBx54aN6Ips6kUikuvvhyrrrqMq677krmzp3L8st/nQsuuJQ11wzBVe51NVuJTTbZnKuvvpxp06ayyirjOe64k0uagK0woK2kRDnXXKgkM2sA/gRsDGzs7i+VuZ9hwGvAy+6+bZnFeSedzoybPn1291v20qtTpnPx7/8NwAU/2YDRzX2TChsoGhqSjBgxjBkzZi80P2Dlqpe6Loz1bGlZwLRpHzFq1FcZNCj/2iZpYN781uIPZGBOM19p55xzBm++6dx66x0LxWs6e/aX7Ljj1hxwwMHsscfeZe0jNNEMjLq++OILHHHEodx442/yOvtWSq6uucnTLrjg0h49vqvPD8DIkcNIpZJTgOXKLmO5D6ygq4DtgBOAoWYWH9j9mrvPMrObgH3dvQHAzDYidGy9C3iP0NH1WGAM0H04OQAUjr4RkepKAcMbB3W6vqEhQWtr33xWq31y+Pbbb/Hmm87jjz9a1giW/jZnzmymTJnCXXfdQSKRYJtttqt2kaRCBkJQsmV0e0GRdZsBTxC+L+InEh8BQ4CfA6OA2cAzwKHu/nyflbSC8qaZV1AiMiB0FRxks4mqBw995cQTj2bmzBl8//vb9njysVKl0+kun7+ezML6xhuvc8QRh7LEEl/h1FN/2tbUkhP6XXSe/UilUgvlVZTrQdWbbwaYfmu++debn3HFnS8DcPaB6/C10eVfF2JhsDCm+stVL3VdGOvZXfq5KwMpzd/X+qKuEyYczL///WKn6++4416++tUlK3Ksc8/9KQ89dH+n6y+//Fq++c3Qx0Kva+nqpfmmLsWDeGVKRKTWnXDCKcyZ0/nIxlImHivVAQcczC67/KDT9YUX0pOBQ0FJlWTzppmvYkFERPpBf1599qtfXbJiWRfpXwvPVIw1Rh1dRURE8ikoqZL8jq5KlYj0J/WlE+m5/vjcKCipEmVKRPpfbprt3IXURKR0uc9NKtV3PT/Up6RK8jq66qxNpF8kkymamobz5ZfhYmSDBw8peWhoJpPodkbYWqG61qZy65rNZlmwYD5ffjmDpqbhbRd37AsKSqpEmRKR6mhuDpd2zwUmpUomk3XT1Kq61qbe1rWpaXjb56evKCipkkxWk6eJVEMikWDRRUexyCIjSKc7n1Y+LpVKsOiiQ/n88zk1f1atutam3tY1lWro0wxJjoKSKslqRleRqgqXgC9tArWGhiSNjY3MnVv7F89UXWvTwlJXdXStkngcouYbERERBSVVo2vfiIiI5FNQUiXq6CoiIpJPQUmVqKOriIhIPgUlVRIPRJQpERERUVBSNfE4RJOniYiIKCipmqwyJSIiInkUlFSJ+pSIiIjkU1BSJQpKRERE8ikoqZL45QfUfCMiIqKgpGqUKREREcmnoKRKspo8TUREJI+CkiqJN98oUyIiIqKgpGryppnXPCUiIiIKSqpFfUpERETyKSipkqyuEiwiIpJHQUmV6CrBIiIi+RSUVIk6uoqIiORTUFIlypSIiIjkU1BSJQpKRERE8ikoqZKMOrqKiIjkUVBSJfE4JKN5SkRERBSUVIuGBIuIiORTUFIl6lMiIiKST0FJlcSzIwpKREREFJRUTV6fEgUlIiIiCkqqRc03IiIi+RSUVEn+kOBMF1uKiIjUBwUlVZKNXyVYiRIREREFJdWijq4iIiL5FJRUiTq6ioiI5OtxUGJmV5iZ9UVh6kl+R1f1KRERESknU7IP8JqZ/dnMdjCzRKULVQ907RsREZF85QQlSwITga8CdwHvmtlJZja6oiWrcRoSLCIikq+hpw9w99nA1cDVZrY5MAE4BzjTzH4PXOnuL5S6PzPbDdgLWAsYCbwNXANc5+5dtmuY2b7AycBY4C3gLHe/o6d1qoZ4i40yJSIiIr3s6Oruf3H3nYFxwDPAj4DnzOw5M9uuxN0cC8wHjge2Be4GLgd+0dWDzGxX4FeEbM33gceB35vZ98qoSr/LoiHBIiIicT3OlMSZWRMhy3E4sDrwGvAHYHvgbjP7qbuf081utnP3z2L3J5vZcGCCmZ3m7vM7edw5wB3ufnLscSsBZwOPllmlfpM/JFgdXUVERMrKlJjZ8mb2S+D/gGuB/wHfc/fx7n62u3+LkOmY2N2+CgKSnH8BjYTmnGLHHwesBNxWsOpWYJ2FoX+LhgSLiIjk63GmxMweArYAZgOTgCvc/e0im94HnFRmuTYCpgOfdrJ+5ej29YLlrwEJQsDyVJnHpqGh76dvyZvRNZPtl2NWUyqVzLutZfVS13qpJ6iutUp1HXjKab5ZHjgamOTuX3ax3SvAZj3duZl9C9if0Gk13clmI6LbmQXLZ0S3RTMspUgmE4wYMazch5cu0T6SOpOlf445ADQ3N1W7CP2mXupaL/UE1bVWqa4DRzmjb1YscbsvgCd7sm8zGwPcCTxPNx1dI4XtHolOlpcsk8kya9acch9estbW9nirNZ1hxozZfX7MakqlkjQ3NzFr1lzS6druQ1Mvda2XeoLqWqtU18pqbm7qdSamnOabNLC+uz9fZN1awPPunipjv4sCDwFzgO3dvaWLzXMZkRHAJ7HlixWsL0tra9+/OQsnT+uPYw4E6XRGda0x9VJPUF1rleo6cJQT0nQ1g2uSMrIUZtYI3At8BdjK3ad185BcX5KVC5avEh3/jZ6Wob/F+7Zq8jQREZHy5ynp7Fd0LeDznuzIzBoIw4hXJwQk73X3GHefQgg8di9YtQchUzO1J2WohrxMSVZBiYiISEnNN2Z2JHBkdDdLmIOkcP6QJmAJ4I89LMNVwHbACcBQM1svtu41d59lZjcB+7p7vLxnECZLexv4M7AD8D1gqx4evyrio2+y2RCYJBO6jJCIiNSvUvuUfAq8Gv0/FniHjiNf5gMvA5f1sAxbRrcXFFm3GfAEkIr+2rj7HWY2FDgFOI4wzfzu7j7gJ06DjtmRTCZLMqWgRERE6ldJQYm730Y0UZmZTQYOc/eK9Ntw97ElbLMfsF+R5b8Gfl2JcvS3wklcM5lsQdglIiJSX8oZEtzjuUeko8JMiTq7iohIvSu1T8kywEfu3hL93yV3f7/XJatxhVPLq7OriIjUu1IzJVOA9QmTmr1L98N+1RDRDWVKRERE8pUalBwAvB37X7+gvZDNZilMjGQVlIiISJ0rtaPrr2P//6rPSlMnirXUKFMiIiL1rpwL8nUQzcg6Fnizi4voSaRY/5HCPiYiIiL1psczuprZRDM7PXZ/LeADwjwm/zWzpStYvppULABJq6OriIjUuXKmmT+Q/InTfgFMB44mXBfntAqUq6bFMyUN0RUVlSkREZF6V05QsgzRBe/MbBFgY+Bkd78cOJMw1bt0IT5x2qCG8BKoT4mIiNS7coKSIUBL9P/60T4ei+6/C4zpfbFqWzxTkgtKlCkREZF6V05Q8j6wUfT/DsC/3X1WdH9xYFbRR0mbokGJ+pSIiEidK2f0zW+BM81sR2B1wsXwcr4F/LcSBatl8TlJUik134iIiEB5Qcm5QCuwAXAXcHls3XjgzgqUq6bl4o9kIkEqGa4MrOYbERGpd+VckC8LnN/Juu17XaI6kI2aapJJFJSIiIhEyulTIr2UC0DimRI134iISL0ra0ZXM9sb2BNYFmgqWJ119+V7W7BalmnLlCRIJTX6RkREBMoISszsRODnwGvAf4D5lS5UrYv3KUmmlCkRERGB8jIlBwNXufvEShemXuSyIolkglQi6lOiIcEiIlLnyulTMoYw6kbK1NZ8kwhNOKDmGxERkXKCkn8C6jPSC20dXZMJUmq+ERERAcoLSo4Bjo2uDixlyGqeEhERkQ7K6VMyCRgFPG9mHwPTCtZn3X31XpeshrU337SPvlGmRERE6l05Qck0YGqlC1JP4s036lMiIiISlDOj66Z9UI66Eu/o2jZ5mkbfiIhIndOMrlVQrKNrVpkSERGpc+XO6Lo4ocPrpsBoYEd3f9XMDgGed/d/Va6ItSdv8rSERt+IiIhAGZkSMxtHmMn1CCALLAcMiVavFi2XLmiaeRERkY7Kab65AJgJrABsDCRi654Cvl2BctW0XFNNIkH7PCXqUyIiInWunKDkO8BZ7v4hIVMS9xGwZK9LVePyMyUafSMiIgLlBSWNwPRO1g0DMuUXpz5komcomWgfEqw+JSIiUu/KCUoc+G4n6zYGXim/OPUhlylJaEZXERGRNuWMvrkB+KWZfQj8Llo22Mx2BX4CTKhU4WpV25DgBKRSUUdX9SkREZE61+NMibtfDfwGuAT4OFr8FPB74Hfu/uvKFa82xfuUaEiwiIhIUNY8Je5+sJndDGwLLEGYdv5+d3+mkoWrVfnXvlHzjYiICJQZlAC4+9+Bv1ewLHUjm+voGpvRVZkSERGpdz0OSsxsaWAj2of+fgj81d3/V8mC1bJ4pkQX5BMREQlKDkrMbBHgemA3woRp8UnTMmZ2K3CYu8+ubBFrT/u1b9CMriIiIpGSghIzSwD3EzIkDwL3Au8RApNlgR2AvYGlCJOrSRdy4Ue8T4mab0REpN6VminZmRCQ7N/J6JrrzGx/4CYz28nd76pYCWtQ3lWC1XwjIiIClD4keHfC6JpOh/u6+yRCNuWHlShYLcsbfRN1dNU8JSIiUu9KDUrWBO4uYbu7gW+WX5z6kMuKJDRPiYiISJtSg5KvAFNK2O6daFvpQi7+SCZQ842IiEik1KBkGFDKqJq50bbShfZp5hMko2nmlSkREZF6V2pH1wTtg0Yqysy+DhwHrAeMB95w9/ElPO5dwsifQk3uPq+SZay0bFYdXUVERAr1ZPK0W81sbjfbNJVRhm8A2wDPETI3Pbkezx+BiwuWzS+jDP2q2DTzaXV0FRGROldqUPJXSs+U9HRm1/vc/R4AM/sV8K0ePPaTaLr7hYqGBIuIiHRUUlDi7pv2VQHcPdNX+x6o8ju6akZXERER6MUF+QaIvczsIKCFkM050d1f7u1OGxp60oJUvlQq2Xbtm2y2/45bDamoQ2/utpbVS13rpZ6gutYq1XXgWZiDknsJ/VDeB5YDTgWeMrM13f2dcneaTCYYMaJvBxANHhKe9sbGQW1BSaIfjjsQNDeX0+1o4VQvda2XeoLqWqtU14FjoQ1K3P2I2N2/mdmjwBuEkTw/KXe/mUyWWbPm9LZ4XZozdwEArS3pthldF7SkmTGjdq9lmEolaW5uYtasuaTTtd1iVy91rZd6gupaq1TXympubup1JmahDUoKuftHZvYUsFZv99Xa2rdvznRre/+RXEfX1nSmz487EKTrpJ5QP3Wtl3qC6lqrVNeBY2A3LvVcotoFKEXbkOCkZnQVERHJqZmgxMyWBL4N/KPaZelOfEbX3OgbzegqIiL1ruzmGzNbCdgEGA3c5O4fR4HBDHfvbpK1+H6GAltHd5cFms1s1+j+k+7+mZk9Dizr7l+PHrMHYcK1h4APCR1dTwbSdJxMbcCJT56W6+iqmEREROpdj4MSM0sB1wP70T79/EPAx8B1wL+AM3qwyyWAOwqW5e5vBjwBpArKOgX4GnApsBgwE/gLcIa7l3LhwKpqm6ckb/K0gdvGJyIi0h/KyZScCuwJHA88DLwSW/cQIVgpOShx93fppi9I4eRt0SyumxbdeCGQa75JJGjPlChVIiIida6cPiX7Aee4+y8BL1g3BRjX20LVuqLXvlFQIiIida6coGQp4NlO1s0DFim/OPUhG7/2TUrTzIuIiEB5QcmnhI6lxRg9vyBf3VGmREREpKNygpIHgVPNbKnYsqyZLQocAdxXkZLVsPaOrrF5SrIKSkREpL6VE5ScQegg+xpwJ2H0zXmEDq+NwDkVK12Nis9T0t7RtZolEhERqb4eByXu/gmwNnAbYUr3NLA6YeTNBu4+vaIlrEHtM7pq8jQREZGcsiZPiwKTQytclrqRa6kJmZLwvzq6iohIvetxpsTMJpjZiL4oTL1on6ekPVOSyWbJql+JiIjUsXL6lFwOfGhmvzezLc1sobgI3kCSd0G+VKLDchERkXpUTlCyMiEw+TZhJM4HZnauma1Q0ZLVsGJDgkFNOCIiUt/K6ejq7n4isAywHfAMcAzwhpn9zcz2r3AZa0588rRkLChRZ1cREaln5WRKAHD3jLs/6O4/AL4KTCRc5feGShWuVmViHV1zfUpAw4JFRKS+lR2U5JhZM/AD4EeEK/fO6+0+a10mPs18Un1KREREoMwhwQBm9h1gf2AnoAl4DjgEuL0yRatd7X1KUPONiIhIpMdBiZmdBewLLA18AlwJTHL3NypctpoVnzwNwlTz6UxWHV1FRKSulZMpOQm4H5gAPOTu6coWqfbl+o4kEyEoSUZBSVqdSkREpI6VE5Qs5e5TK16SOlIsU9KChgSLiEh9K2dIsAKSXsrGZnSF9uBEfUpERKSelZQpMbObgXPcfUr0f1ey7v7j3hetdsU7ugJtI3AUk4iISD0rtflmM+Cy6P/Nga5+PvXT2o22eUqS+ZkSNd+IiEg9Kykocfdxsf/H9llp6kTbPCW55puEghIREZFyrhK8jJkN6mRdg5kt0/ti1bZiHV1BfUpERKS+lTOj6xRgzU7WrR6tly50yJSo+UZERKSsoCTRxboU6lPSrWxbpiTcb8+UaJ4SERGpX+Ve+6ZD4GFmQ4DvAxoy3I1cQqRwSLAyJSIiUs9KHRJ8JnBGdDcL/N3MOtv8xgqUq6YVNt+0ZUp0QT4REaljpQ4Jfh64mtB08xPgj4Tr3sTNB14Gbq1Y6WpUYUfX9kxJ1YokIiJSdaUOCX4IeAjAzIYBZ7u7OrSWqT1TEu6n1HwjIiLS82vfuPv+fVGQetJh8rSEhgSLiIiUc0E+AMxsPLAy0FS4zt1/05tC1br2aeYLmm/Up0REROpYj4MSMxsK3Ev7dPO5IcLxX1QFJV3IZjvp6KpOJSIiUsfKGRJ8OjAW2IQQkOwMbAH8CXgT+GalClercrFHQte+ERERaVNOULID8Avgmej+++7+uLvvBrwIHFapwtWqbCdXCVafEhERqWflBCVjgTfcPU1oshkaW/c7YMcKlKumtY2+KciUqEuJiIjUs3KCkpnAsOj/T4EVYusGxdZJEdlstq3zTcc+JYpKRESkfpUz+uZlYEXgYWAycIqZvQksIMz6+p/KFa/2xEfYFA4JVp8SERGpZ+UEJTfRnh05FXgKeDK6PxPYugLlqlnxATaFQ4KVKRERkXpWzuRpf4j9P8XMVqR9ePAz7j69guWrOfmZknCrGV1FRER6MXlajrvPBu6rQFnqQjzw6Jgp0TwlIiJSv8rp6Cq9kC3SpyQVpUyUKRERkXpWUqbEzDLkz9jalay79zoDU6vicUfHaearUSIREZGBodTg4WxKD0qkC/FsSEKTp4mIiLQpKShx95/2cTnqRq6jayIBicJMiYISERGpY1VvZjGzrwPHAesB4wmzxY4v8bHHAROAMYT5U4539yf6qKgV0Tabay5NguYpERERgfKuErxPd9u4e0+uEvwNYBvgOULH25I630YByXnAKYRr7hwEPGRm67j7yz04fr/KZUpy2RFQ842IiAiUlyn5VSfL47+oPQlK7nP3ewDM7FfAt7p7gJkNAU4DLnX3i6JlTxKyJacCP+zB8ftVLu7Iy5So+UZERKSsIcHjivytDZwLvEUJQUWcu5czOccGwKLAbbH9pIHfA1ubWaKzB1Zbtu1ifO3L2jIluiKfiIjUsXJmdH2vyOL3gH+a2SDgSGC/XparOytHt28ULH8NWARYCvhfuTtvaOi76VsSsevdpFLhOA2p9uMNGlT82Nls+2idznS3TTX30VbX6LkdqOWsxD56UtdKlKNadY3XM5FY+F+3rtbXc10rUY56qmsl9tGfdR1oKt3R9XHgD91u1XsjgPnuPrdg+YzodiRlBiXJZIIRI/ruQsez5qeB8AZpbm4CYOjQwQC0pLMsyHR8lyWAZCpBOt35O6i7baq+j0yWudPnROuTA7ecldhHiXWtRDmqWteonmF9YuF/3bpaX6d1DbVdiN+j3a2vcF0rUZf+qivA0MYGhke/PwNFpYOSZYF0hffZmWKvRqKLdSXJZLLMmjWn+w3LNHNm+75nzZpLc3MTrS2tAEydOYfnX/mww2OGNjaw9Jhm3vm/mbS0FG/t6m6bau8jmUwwZMggUglYaonhA7ac/VnXSpSjmnXN1XP+/BYaB6cW+tdNde1Y10wmu1C/R/u7rpWoS3/VdVBDkpXGjqR1QUvFMibNzU1tGZlylTP6ZuMii4cAqwEnE7IlfW0G0Ghmje4+L7Z8sdj6srW29t01aHJvkEQC0un847SmM8yd39rhMalUgmw2y/z5Gea3dFxfyjbV3kcqmSCZStGazQzocvZnXStRjmrWNVfPefNboyaNhft1U1071jWdyS7U79H+rmsl6tJfdU1nUmQzWVpbs3mXP6m2cjIlT9AxE5HLUDwGTOxNgUr0enS7MvCv2PJVgC+A/+uHMpSlbUhwrLGv/SrBVSmSiIjIgFBOULJZkWXzgHfd/ZNelqdUzwCfA7sTBSVmlgJ+ADzo7gMn7CtQLChpv/bNgC22iIhInytn9M2TlSyAmQ0Fto7uLgs0m9mu0f0n3f0zM3scWNbdvx6VYb6Z/Qw4z8w+I0yediCwHAN4jhKAbJQNKTYkeCCl0ERERPpb1aeZB5YA7ihYlru/GaG5KEXHsl5MaDY6AvgKYeK0rQfybK7QdaZEMYmIiNSzcjq6pgj9RvYiZDYaCzbJuvuipe7P3d+lvU9KZ9tsWmRZFrgw+lto5LIhedPMJ9R8IyIiUk6m5HzgWODfwJ+BBRUtUY0rekE+Nd+IiIiUFZTsBZzv7qdUujD1IHd5m0TRa99Uo0QiIiIDQzmznDQRhv5KGdqvEty+LKXRNyIiImUFJY8C61a6IPVCzTciIiLFldN8cwTwgJnNBh4Ephdu4O4dlkmQKdbRtS1TUpUiiYiIDAjlBCWzAAcuif6KSZVdohqX6zeiTImIiEi+coKSawkzp95DmO5do296oG1IcPyS0pqnREREpKygZEfgZHe/qNKFqQddNt+o/UZEROpYOR1dW8i/CJ70QC7wKDYkWM03IiJSz8oJSv4EbFnpgtSLYpmSZEIdXUVERMppvrkNuMHMBgEPUHz0zYu9LVitKtbRVfOUiIiIlBeUPB7dHkkYHhyXALJo9E2nMkU6uqr5RkREpLygZP+Kl6IO5PqQ5OKOZDJBLlnSkNI08yIiIj0OStz9131RkFqWBubNawFg7oJWIPQf+XxOCwsyc5izIEQjWZQpERGR+lVOpkR6IJFIMG9eC6+9O52W1gwffPolALNmL+Dlt6bS1DSYz7+YC0BWmRIREaljPQ5KzOzmbjbJuvuPyyxPzWppzbCgJU1ra5QVyWZZ0JKmYVCmbZiwOrqKiEg9KydTsjl0aGcYBQwHZkZ/0olcZ9bY4Ju2/iYKSkREpJ6V06dkbLHlZrY5cDWwWy/LVNNyLVgghboAAB60SURBVDR5k6dF/2azIWiJrxMREakX5UyeVpS7/wW4ErisUvusRV1lSqBjCkpERKReVCwoibwGrFPhfdaUXAtNsWnmw3qFJSIiUp8qHZRsAkyt8D5rSlumJLYsPrurYhIREalX5Yy+OaPI4iHAasD3gQt7W6haVjRTEotQ1NlVRETqVTmjb35aZNl84F3gDBSUdKlon5J4843mKhERkTpVzuibSjf51JVcHiTeZBNvylGmRERE6pUCjH7W3nzTviyRSOQNCxYREalHJQUlZjbCzO40s2272GbbaJtRlSte7SnWfAPtI3CUKRERkXpVaqbkQGB14OEutnkYWBU4vLeFqmVtmRLyo5JcUKIhwSIiUq9KDUp+CNzg7q2dbRCtuwHYvhIFq1WdZkoSuaCkv0skIiIyMJQalKwIvFDCdi9G20oncjFH4VTyar4REZF6V2pQ0gC0lLBdCzCo/OLUvs4yJamkMiUiIlLfSg1KPgJWKWG7bwAfl1+c2pcpMnkatDffKFMiIiL1qtSg5EngJ2bWaRYkWncYMLkSBatV3Y2+UUdXERGpV6UGJZcAKwF3mdmShSujZXcDFm0rnSg2zTzEOrpqRlcREalTJc3o6u4vmdnhwNXAFDP7JzAlWj0OWIsQ4Bzm7i/3SUlrRC4TUhgNqqOriIjUu5JndHX3G4CNgUcJF9/bI/pbjTBHyUbufmNfFLKWdJopUUdXERGpcz269o27PwtsZ2ZJYHS0eKq7q9GhRO1DgvOXJ6PwUJkSERGpV+VcJZgoCPm0wmWpC50OCU6oo6uIiNQ3XZCvn3XWfDNoUAqA1rSCEhERqU8KSvpZZ5mSpsEhKFnQku7vIomIiAwICkr6WWcX5GsaElrSFrSqe46IiNQnBSX9LNNJpqRRQYmIiNQ5BSX9rLML8rVlStR8IyIidaqs0TeVZmYrApcDGwGzgduAk9x9bjePewLYpMiqld39jUqXsxI67VMyJPQpaVGmRERE6lTVgxIzWwz4C/AesAuwBPBLYBSwdwm7eBo4rmDZuxUsYkV1NvqmcbAyJSIiUt+qHpQAhwAjgDXcfSqAmbUCvzOzc9399W4eP9Pd/97XhayUtkxJwXJ1dBURkXo3EPqUbA08lgtIIncC86N1NaU9U5K/XH1KRESk3g2EoGRlIC8b4u7zgbejdd3ZxMxmm9k8M3vSzDbui0JWSi4oSXbo6BrNU6JMiYiI1KmB0HwzAphZZPkMYGQ3j30S+A3wJrAkoW/JY2a2SXSdnrI0NFQuVkskIJFMkIr+stH4m1QyQTK64E0ymaCpcRAQOrqmkgUX60skSCQSJFOQShc2/JS2TbX3Ea/rQC5nf9a1EuWoZl3b65ms+nPe1/uo17pCZqF+j/Z3XStRl/6qayqZIJFM0NCQIJstfpxqGAhBCbSPlI1LdLK8jbufGb9vZvcDrwKnU2bTTzKZYMSIYeU8tFMLMnNoahpMw6BMWwfXxsbBNEaByJAhg1hk6GAgBCWNjYPbrhoM0NTYQENDiqbGwTQ0FM+kdLfNQNnHkCGDFopy9kddK1GOgVDXxsZBA6Ic/bGPeqtrJcpRT3WtxD76q66DGpI0NQ5mscWGFj1GtQyEoGQGIVtSaDEKmnW64+6zzewBYNdyC5PJZJk1a065D+8gkYC581qYO3cBC1rSpKNr2yxY0MK8eS00Ng5i/vwWBi3W2PaYz2fNZUg07TxAIpuhtTXN3HkLWLCgeJ+T7rap9j6SyWRbXQdyOfuzrpUoRzXrmqvnvHktVX/O+3of9VrXTCazUL9H+7uulahLf9V18KAUc+ctYObMLJW6DmxzcxOpVO9aGgZCUPI6BX1HzGwIsDxwcxn763UeqrWC/ToSiQTZTJZ09JcbfZPNQiYTjpPJZEkmEzSkErSms8xd0JrXhJTJhsdl0pDOFH/3dLdN9ffRXteBXc7+q2slylHduubqmSGTTdbA66a6FtY1ncku5O/R/q1rJerSX3VNZ7JkM1laW9t/lwaCgdDR9UHgO2Y2KrZsJ2BItK5kZjYM2Ab4R+WKV1mdTZ4GMHhQ7qJ86uwqIiL1ZyBkSq4DJgL3mNk5tE+e9rv4HCVmdhOwr7s3RPc3InRsvYsw8dqSwLHAGGC3fq1BD3Q2zTzA4IYUc2hlQauGBYuISP2peqbE3WcCmxOml/8TISC5DTioYNNU9JfzESGb8nPgEeDKaNlG7v58Hxe7bJ3NUwIwZFB4OZQpERGRejQQMiW4+3+BLbvZZj9gv9j9t4Ct+rRgfaD9KsFFMiWDdP0bERGpX1XPlNSbrjIlbX1K1HwjIiJ1SEFJP8t1dE2q+UZERCSPgpJ+9v/t3XmQXNdd6PHvvb337CONxrIU24qUHFsEQgoXJFBAJRRZDCEvjySuvLx6fhSE5Q/2ACFVIUDYiwoECqgQCCZFMLyQhCSOEie2YxLLft4TyZZ0LI2k2TQzmrX35d6+lz/O7Z6eVs8iebqne+b3qVLNzF16ztGZuf2b39lqmZImM5ejYcmUCCGE2LskKGmz6jLzMiVYCCGEWEuCkjZbHVPSbKBr0H0jA12FEELsQRKUtNlWFk9zHOm+EUIIsfdIUNJmG2VKYrUxJZIpEUIIsfdIUNJibsVjKV2sfb1xpqQ6+0YyJUIIIfYeCUpa7BNfOsuH/vHJWmCy4eybiGRKhBBC7F0SlLRYKlcGYDFVxPf9ur1vrr22fkXXTtq1UQghhGgHCUparC8ZAaBQrlAfZjTfkG+1OWSpeSGEEHuNBCUt1p+MAlAsu9QnP5qt6BoO2YSCE7JWiRBCiL1GgpIWq2ZKiqXKmi6ZZpkSqF+rRAa7CiGE2FskKGmxviBTUmjIlKwnKtOChRBC7FESlLRYf0/QfdOQKWnWfQMQCcu0YCGEEHuTBCUtVuu+aciUrN99szoDRwghhNhLJChpsb5EXaaE+jElza+P1jIlEpQIIYTYWyQoabFqpsSpeLgVGegqhBBCrEeCkhZLxsPYwQCSQskF1s+SAESqA10lUyKEEGKPkaCkxSzLojcRLKBWDUqaLDFfJZkSIYQQe5UEJW1QDUqKJRNobJQpqU4JloGuQggh9hoJStpgdan5zbtvVncKlqBECCHE3iJBSRtc032zQVSyuniadN8IIYTYWyQoaYPVoGQr3TeSKRFCCLE3SVDSBrWl5oNMib1BVBKRga5CCCH2KAlK2uDa7pv1r60NdHW8NcvSCyGEELudBCVt0Jts6L7ZwpRgH7PgmhBCCLFXSFDSBtVMiRdkPjbKlIRsq9a948i4EiGEEHuIBCVtUJ0SXLXR7BvLsuoWUJOgRAghxN4hQUkbVDMlVRtlSqB+Bo4MdhVCCLE9ckUHz+vssYoSlLRBIhZeE4hslCkBiESqa5VIpkQIIcRLN7uU5977z/C0nt/pomxIgpI2sCyLRDRc9/XG10umRAghxHaauppd87FTSVDSJvFYqPb5JjEJUcmUCCGE2EZLmRIAqVy5o/dWk6CkTeJrMiUbhyXVTIkjmRIhhBDbYCldqn2+ki1tcOXOkqCkTRJ1mRJ7s+4bmX0jhBBimxTLbm3xToDljAQle971ZUpMAFP/QySEEELciPosCUhQIoB4tG5MySaZkuH+OABXlwutLJIQQog9oBqEhII0/VK6uJPF2ZAEJW2SiG09U3JgKIFlQa7oks07rS6aEEKIXWJ6Psf4bGbNsWpQ8vJDA7WvO3VvNQlK2mRNpmSTayNhm/0DJlsyu5Rfc+7s5WW++tQkxbIMghVCCLEqX3R5+NkpTjw+TqpuMGs1M3Ls8CAh28Kt+KQ79A9eCUra5HrGlADcNJwE1gYljuvx3Pl5ZhfzjE2ntr+QQgghutbEXAbfB9+H02OLAFQ8j1SuDMDIUILB3hgAS6nO7MKRoKRN1qxTsnlMwmhdUFJNs01czeBWzOeXZ9LbX0ghhBBda2JudWG0alCyki3j+xCL2PQmIgz3B0FJh44rkaCkTa5nRVcw40psy6Tjqmm2+uzIYrpEOoh+wWRRPnH/GZ44M7t9hRZCCNFxzl5e5iP3PcdK3SyaYtllbnk1sz42naJQclkOZt4M9cexLKsWlCxKULK3RSN2LRjZSvdNOGSzfzABwPR8lkLJZTJYHri663B9tuSFS0u8cHGJU2OLzMusHSGE2JVKToWTp2aYns/x5Nm52vHJq1l8H4b7Y4wMxvF9GJ/N1Aa5DveZYKQ6u7NxmnCn6IigRCn1SqXUV5RSOaXUVaXUR5VSiS3ee49S6pxSqqiUel4p9c5Wl/dGWJZVG+y6hUQJsNqFMz2f4/mLi3iez0BPlO98+T4ALs1k8H3fjDV5cXWTpW9dWFjzOtPzOf7lAc2zHb4RkxBCCGM5U+SLJy9z5vLSmuNnLy/XFtYcn8uwGIwNqXbd3DLax5Gb+wG4NJNmKWPOV4ORanCSLTjki5032HXHgxKl1CDwMNAH/CTwPuA9wMe3cO87gHuBzwFvAR4C/l0p9cZWlfelqA523UqmBOCmYROXXZnP8VwQUNx2sI9bRnuxLYtUrsxKtsyLkysUShUGeqJYFsws5mtrnOSLLo+emiGdc/j0Q+evWV54bDrFE2fmZPM/IYRoM9/3OXN5idNji3h1U3Qdt8JDz0yxnCnxzLl55oIJD2WnwtnxZQD2BTM0T40tUnYqzCzkALhltJejwdTfKws5llJBpiTotolGQvQmTLZ9ej7Xhlpenx0PSoCfA4aAt2mtv6K1/iTwS8B7lFJ3bHLvh4FPa61/W2v9da31LwNfA36/tUW+MbFqpmSLqZKRwQS2ZZErurw4sQLAkYP9RCMhDo30AHBhKsULl0wk/abX3sIrXzYIwLcvLOD7Po89P0MpCDjKrsfXn53C88wP/7mJZU6enkVPrPDQM1M47mpgspwpceLxcb757SvXBCyO6zF5NUvZlUBGCCHABBgzi7lrsg++73N6bJH7H7u8Zode3/d5+tw8T5+b57nzCzz+/GxtUsOXTl6udbv4wKOnZig7Fc5cXsZxPYb7Y/z0W49jYbptTo0t4vkw0BNlsDfGUF+M0eEknu/jVDxsi9qsG4ChIFvSiUFJePNLWu4u4EGtdX2fw2eATwTnzja7SSl1BLgd+EDDqX8F/kkptb/hNXdcPFINSrYWlYRDNiODceaWC/jA/oE4/T1RAI4c7GPyarYWNff3RLjz9gPYWLw4ucLMYp6Tp2e5spAnZFvc9bpbefDpKRZSRb59YYG+ZJQnz1wFzF488ytFTjw+wdHDg1yaSfPIc1O4FZ+FVJHFdInXv+YQvckI58aX+Y9HLpAtOCTjYV57fJTDB3oBmFvK8+DTixRKFe64dYgjN/fXVhCcXykw8eICyViI/UNre+aWMyXmlwscGErQG4yXqUrnypTdCsP9ceyG/7d8ycX3PcKha2PrSkX2DRJit/E8H9/3r3mGer5PNu80XRAsnSvjVjx6Emvf7goll5nFPPsHkwz2rJ5zXA89sczsUp7DIz0cGunBsix832d8NsP9J8fJ5Mu86sgwtx3sw7IsVrIlHn9+jvmVApGwzVvzLsO9McpuhZOnZmvjAR9+dppMweHIoQGeOne19vy2gLHpNOGQzbHDA3zz2zMA/NCrD/Lc+QUyeYeTp2drS0TcefsBRoeTHD00wIXpFGcum9e5ZbS3Vo/vPLqvlmEZ6I3VnsVggpLJq1mm5leDpE7RCUHJHZgApEZrXVJKjQXnNroPrg1azmDa+Hbg0e0q5HaoTgveaqYEzLiSuaArppqSAzg00ks4ZNWmCH+POkAoZNOXjHLs0ADnp1JcvJIOzo3wstFe3vmGY3zyy+c4fXGpNq7ljluHuO1gH197apLp+Rx/cd+3at9vdChBJu+QzpU58f/HOTzSw6UZs1KgRXWhnmluvakPx/W4srAadT/2/CynxhZ5xeEBpuazzK+sjvR+6uxVjh4aoC8ZMX2edQOuRgbjfP93HWRiJsulmRQrWTPDKB4N8bIDvYwOJ0xX1NfHmF8uYFsWo8MJbt7fQzwaYm65wPxygVSuzGBvjANDcQ4MJSk7FeZXCiykirgVj9HhJMdvy+A4HgvpAoupIsuZErFIiH0DcW7e30O24HL6wiLzqQK5gkN/MsJQf5zB3ihlxyNXcnj4mWnSuTJ9yQiDvVF64hHyRZdcySWVLbFvwMyiSsbDhGyLdN4hkytTKFfoiYcZGUxQcjwuTqVYzhTJ5B0836cvGaUvGWGoL8b8SpHzEyuk82UiYTOtrzcRwbYtnIrHC5eWmVnIEYvY9CQi9MQjOG6FXNElV3AIh23G57IUCi6WZUbp54pmg65IyGa4P0ahXGH6ao5UtkS+5FJ2KiRiYXriYRKxMLZtMT2f49KVNG7FIxkP05uI0NcTZyVTwHE9nr+4RDbvEI3YJIMVjPMl830c12OgN8pKtsxyqkS+5JAPyoBlVjwe7o/hVnzOT66QzpcolitEwzaJWLi2InLF97kwnWZ2IUckOBePhii7HoWii+t56IkVCkWXSNgmErYpBGUolitEIyEG+6KUXY+J2SzpnKmvW/Fq32eoL0Ym73B+MkUmXw7aL8LQQBzPreB6PpdmMkzOZgiFLJKxMMl4mIrnm/oWXSzLYnw2a/7/QxalcoV8ySVfdLFtGOqLkyu6zC3mSWVL5IqmfLFoiJ64eb1oOMT0Qo6LU2lKjksyHqm1R6Hk4lQ8ntXzpHMOiViInkSEsG2RLTpk8w7FcoW+nggzi3lyBZdiySGTd8gUHNyKR18iwv7BBGXXQ48vs5QpkQl+xvYNJElEbWzbolh2efrcPNPzWRKxMP09UfqTERzXYyVbJltwiIQniYZtepMReuMR0vkyy5kSK9kyiVjIPLt8C9/3WEyXWEoXyRYc+pNRhvtj3DzSw8xintMXFplbzuNWPPYNJBgJ/hBL58t86bFxJuYyxCIhRoeTjA4lCNkWVxZyzCzmKbsefd+4yE3DSQ4MJUjlykzMZWuzFHviYV79ihFClsXFmRRXFnJUY5ieeJiX39xPJBLin0+cI1c0+469OLlCXzLCsUPXPse+eWqG0xcXuWlfkhcnVggS0Diux2cfGWN0KEGxXCGVK2NbFjfvTzI1n+Ops1eZvJpldtEEDN93/ACRcIhHT82gJ1Y4P2lmWX7HkWFuO9hPTyLCV56YqAU2g71Rjh4yY0Ze84oRLtTNyrzlpr7a5991bB8PPjUJrGZGqqpdOdMLnZcpsXZ6qVmllAN8UGv9Jw3HHwWuaq3/5zr3vQf4F+Cg1nq27vgx4DymO+gL11mcgu/78Wr3xnbxgsGopXKFklMhEraJR8NYFlhYYEE4ZOG4Ho3NUfE88sEvSG8yYq4PFIMHk21Z9CYjRMI2juvheT7ZgkkhhkPmwW1bEA7bpHNlnGCQVLUcjd8HzKaAsWgI3/cplFwqdf8n0UiIaNim7HrXdO1EwyEiYTvIYqytTCRs43n+mteqCtlW0+NgAqDOXBBZCNHNbNvC9/xrni+2ZREKWbiud825eDSMj0+5XFlzLhyyiUdDuBWfUtmtnbMsi2QshG3bOG5lzWrcsWiotgFr/bmQbdGTiNTeD8pOpdYNn4iFiYZtwsHzvhr025a5x5QfQiGblUwJz/eJRUPEIiEsLHz8Ne8RI4NbmlOyJbZtYVlWEbjhF+2ETAk0f8/Z6ntR4zXWOse3omSZH8aZG7h3XSEgEg6RjEc2vC4SDjU93t8Ta3q8+gPY7DV6k9Gm9+wbWP9nZb3vs95rbaSv5/rvEUKI3aH5sxQ2eg9Y/1yzZz1s/r5yIJjB2cyNPNe34CDwkuYad0JQsowZ6NpokHXGk9TdR3DvXN3xwYbz12Nw80uEEEII0QqdMPvmLA1jR5RSMeAoGwcl1XON406OY7Ik57argEIIIYRovU4ISk4AP6KU2ld37O2Y/NeJ9W7SWl/CBB53N5x6N/Bkp828EUIIIcTGOqH75mPALwKfV0p9GDgAfAT4lNa6lilRSv0jcI/Wur7Mv4NZLG0Msz7J24A3Am9uV+GFEEIIsT12PFOitV4B3gDkgM9iApL7gPc2XBoK/tXf+2ngp4B3AA9gApK7tdZfbXGxhRBCCLHNdnxKsBBCCCEEdECmRAghhBACJCgRQgghRIeQoEQIIYQQHUGCEiGEEEJ0BAlKhBBCCNERJCgRQgghREeQoEQIIYQQHaETVnTdc5RSrwT+CvhBzKJx9wHv11oXdrRg20wp9X+Bf2py6k+11u9vc3G2lVLqGPA+4LXAq4BzWutXNbnuHuC3gduAC8DvBYv+dYWt1FMp9Qjww01uv0Nr3TV7UCml3gm8B/geYBgYA/4O+JjW2qu77i7gDzH7bk0BH9Fa/237S3xjtlJPpdS9wD1Nbn+L1vorbSrqS6aUehPwAcyeaP3ANPCfmN/DVN11Xd2msLW6dkO7SlDSZkqpQeBhYBz4SVaX1d8H/O8dLForvRlI1X09vVMF2UbfAfwY8AQm43hN1lEp9Q7gXuBPgK8C/wOzLUKqi1Yd3rSegZOY4KXe5dYVqyV+HfN7+RuYncdfj/nj4eXBMZRSrwM+D3wS+DXgB4C/VkqVtdb/sBOFvgGb1jNwERO81Ntok9RONAw8BvwlZuf4VwG/G3x8I+yaNoUt1DXQ0e0qK7q2mVLqtzB79txa3TRQKfW/gE8Bx+v3++l2dZmSkd22QaJSym74q/LOJhmEs8BprfW76o49AAxorV/bzvLeqC3W8xEgq7X+8faXcPsopUa01vMNxz4C/AIwqLUuKaW+DAxrrb+v7pq/B34cOFyfUelUW6znvTRp691AKfVe4O+BQ1rrK7uhTdfTpK730uHtKmNK2u8u4MGGN+nPAKXgnOgCmz2olFJHgNsxXXP1/hX4XqXU/laVbTt18wP5ejW+UQeeA+LAsFIqhtmn698arvkUcBB4TWtLuD02q2ebi7MTFoOPkd3Sphuo1XVHS3EdpPum/e4APlF/IPjLZCw4txu9ELwJjwMfB/5Ma13Z4TK1WrUtGzNfZwALE7A82tYStdYPK6VymE0znwA+qLX+xg6XaTv8ILAEXAUUEKV5m4Jp82faV7RtVV/PqqNKqRUgCZwGPqy1/s+dKNxLpZQKYd6Yj2My1V/UWo8rpY6zy9p0vbrWXdLR7SqZkvYbAlaaHF9m9/2VMgN8CPg/wFuAE8AfAB/dyUK1yVDwsbGtl4OPu6mt/wv4ZczYoXswD7sHg776rqWUuhOzC/lfBEH0rmzTJvUEkzl5H2Yc1LuABeBzwTipbjQOFDABxgzw7uD4bmzT9eoKXdCukinZGc0G8ljrHO9aWusHgAfqDn1VKVUAflUp9Yda65kdKlo7Nbaptc7xrqW1/lD910qp+4EXgA/SpV2SSqmbMN2qTwJ/2nB6vbbrujZdr55a6482XPcFzCDK3wf+o51l3CZ3Ab2YgdsfBL6olPrRuvO7pk1Zp65a60o3tKtkStpvmdXovN4gq9H5bvb/MCn+797pgrRYtS0b23qw4fyuo7XOAV/CTDntOkqpAeDLQB74Ca21E5xar02HGs53hQ3qeY1gbNFngDuUUok2FXHbaK1Paa0f01p/HHg7ZsbR29llbQob1rXZtR3XrhKUtN9ZGsaOBIOtjtJB07JayNr8kl2h2paN44SOY/766pr1O25QV7azUioOfAEYBd6stV6sOz0GlGneptBFv7+b1HM9XdmmTXwLqADH2EVtuo76uq6no9pVgpL2OwH8iFJqX92xtwOx4Nxudzfml+S5nS5IK2mtL2ECj7sbTr0beHK3TZGup5Tqwaxt8tROl+V6KKXCmEzeqzFv1PWDA9FalzBrDL2r4dZ3Y/ruu+JnerN6rnOPDbwDeGEXLPL4Oky29uJuadMN1Ora7GQntquMKWm/jwG/CHxeKfVhVhdP+9RuWqMEamtyPAQ8Hxz6CeBngY9qrWd3rGDbQCmVZHW8xK1Af91gsf8Kpl3+DmaxtDHga8DbMIsYvbnd5b1Rm9UTM4vofcDnMAPsbsYsznUT8M72lvYl+xvgrcBvAkmlVP1aMme01mlM3/s3lFIfx0wb/QHgvcDPddH06Q3riem6uBcznX0s+PoXgDsxCz52DaXUZ4GngVOYwZ+vxtT7FGa1U9gdbbppXZVSt9IF7SpBSZtprVeUUm8A/hr4LKY/9z7gt3a0YK1xDvgZ4DAmK/ci8CuYune7A0DjcvHVr18PPKK1/nTwpv4BzBv3BeDuLlrNFTav5xQmy/fHmFWJc5iBcz+vtX6yXYXcJm8KPv5Zk3PVNn1cKfU24I8ws8qmgF/qspU/N6vnKSCNCapHMN0bT2OWIn+gyT2d7ElMtvL9mGfQZcxiYn+utS4D7JI2hU3qqpTK0AXtKiu6CiGEEKIjyJgSIYQQQnQECUqEEEII0REkKBFCCCFER5CgRAghhBAdQYISIYQQQnQECUqEEEII0REkKBFCCCFER5CgRAghhBAdQYISIYQQQnQECUqEEEII0REkKBFCCCFER/hvV9DaUVuNjEUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "# Nsamples = 20\n",
    "# mkdir('snr_weights')\n",
    "# KLD_vector = net.get_weight_KLD(Nsamples)\n",
    "# np.save('weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(KLD_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(KLD_vector, norm_hist=False, label=name, ax=ax, hist_kws=dict(cumulative=True))\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Cumulative Density')\n",
    "ax.legend()\n",
    "plt.title('KLD CDF: Total parameters: %d, Nsamples: %d' % (len(weight_vector), Nsamples))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate pruning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]\n",
      "-inf\n",
      "params: 2395210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete proportion: 0.000000\n",
      "\u001b[34m    Jtest = 0.109638, err = 0.026400\n",
      "\u001b[0m\n",
      "-0.020493 nats\n",
      "params: 2156121\n",
      "Delete proportion: 0.100000\n",
      "\u001b[34m    Jtest = 0.106574, err = 0.025500\n",
      "\u001b[0m\n",
      "-0.003850 nats\n",
      "params: 1916669\n",
      "Delete proportion: 0.200000\n",
      "\u001b[34m    Jtest = 0.102950, err = 0.027100\n",
      "\u001b[0m\n",
      "0.018795 nats\n",
      "params: 1437948\n",
      "Delete proportion: 0.400000\n",
      "\u001b[34m    Jtest = 0.098232, err = 0.026900\n",
      "\u001b[0m\n",
      "0.064575 nats\n",
      "params: 958098\n",
      "Delete proportion: 0.600000\n",
      "\u001b[34m    Jtest = 0.096836, err = 0.027300\n",
      "\u001b[0m\n",
      "0.154137 nats\n",
      "params: 478676\n",
      "Delete proportion: 0.800000\n",
      "\u001b[34m    Jtest = 0.099576, err = 0.028700\n",
      "\u001b[0m\n",
      "0.227624 nats\n",
      "params: 239861\n",
      "Delete proportion: 0.900000\n",
      "\u001b[34m    Jtest = 0.110249, err = 0.032700\n",
      "\u001b[0m\n",
      "0.378996 nats\n",
      "params: 23902\n",
      "Delete proportion: 0.990000\n",
      "\u001b[34m    Jtest = 0.147214, err = 0.041000\n",
      "\u001b[0m\n",
      "0.417444 nats\n",
      "params: 11964\n",
      "Delete proportion: 0.995000\n",
      "\u001b[34m    Jtest = 0.156281, err = 0.043700\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delete_proportions = [0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]# 0.9999, 0.99999, 0.999999, 0.9999999, 0.99999999] # \n",
    "# keep_proportions = 1 - delete_proportions\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "Nsamples_predict = 100\n",
    "# Nsamples_KLD = 20\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "err_vec = np.zeros(len(delete_proportions))\n",
    "\n",
    "print(delete_proportions)\n",
    "\n",
    "for idx, p in enumerate(delete_proportions):\n",
    "    if p > 0:\n",
    "        min_kld = np.percentile(KLD_vector, p*100)\n",
    "        print('%f nats' % (min_kld))\n",
    "        og_state_dict, n_unmask = net.mask_model(Nsamples=20, thresh=min_kld)\n",
    "#         print(net.model.state_dict()) # for debug purposes\n",
    "        print('params: %d' % (n_unmask))\n",
    "    else:\n",
    "        print('-inf')\n",
    "        print('params: %d' % (net.get_nb_parameters()/2))\n",
    "    \n",
    "    test_cost = 0  # Note that these are per sample\n",
    "    test_err = 0\n",
    "    nb_samples = 0\n",
    "    for j, (x, y) in enumerate(valloader):\n",
    "        cost, err, probs = net.sample_eval(x, y, Nsamples_predict, logits=False) # , logits=True\n",
    "\n",
    "        test_cost += cost\n",
    "        test_err += err.cpu().numpy()\n",
    "        test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    test_cost /= nb_samples\n",
    "    test_err /= nb_samples\n",
    "\n",
    "    print('Delete proportion: %f' % (p))\n",
    "    cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))\n",
    "    err_vec[idx] = test_err\n",
    "    \n",
    "    if p > 0:\n",
    "        net.model.load_state_dict(og_state_dict)\n",
    "    \n",
    "    \n",
    "plt.figure(dpi=100)\n",
    "plt.plot(delete_proportions, err_vec, 'o-')\n",
    "\n",
    "np.save(results_dir+'/kld_proportions', delete_proportions)\n",
    "np.save(results_dir+'/kld_prune_err', err_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "source": []
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   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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